From 6e6b4e697d4c762190a0677399df376389e9dda2 Mon Sep 17 00:00:00 2001
From: Zeeshan Siddiqui <mzs@microsoft.com>
Date: Fri, 5 Apr 2019 02:03:10 -0700
Subject: [PATCH 1/7] Add time series samples for stateful prediction engine.

---
 .../TimeSeries/DetectChangePointBySsa.cs      |  99 +++++++++++------
 .../DetectChangePointBySsaBatchPrediction.cs  |  90 ++++++++++++++++
 .../TimeSeries/DetectIidChangePoint.cs        |  78 +++++++++++---
 .../DetectIidChangePointBatchPrediction.cs    |  86 +++++++++++++++
 .../Transforms/TimeSeries/DetectIidSpike.cs   |  66 ++++++++----
 .../DetectIidSpikeBatchPrediction.cs          |  77 +++++++++++++
 .../Transforms/TimeSeries/DetectSpikeBySsa.cs | 101 +++++++++++-------
 .../DetectSpikeBySsaBatchPrediction.cs        |  92 ++++++++++++++++
 8 files changed, 579 insertions(+), 110 deletions(-)
 create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
 create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
 create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
 create mode 100644 docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
index 0e14dd6f6c..6ce526e715 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
@@ -1,7 +1,9 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -24,6 +26,7 @@ public SsaChangePointData(float value)
         }
 
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
+        // It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
         // The estimator is applied then to identify points where data distribution changed.
         // This estimator can account for temporal seasonality in the data.
         public static void Example()
@@ -32,7 +35,7 @@ public static void Example()
             // as well as the source of randomness.
             var ml = new MLContext();
 
-            // Generate sample series data with a recurring pattern and then a change in trend
+            // Generate sample series data with a recurring pattern
             const int SeasonalitySize = 5;
             const int TrainingSeasons = 3;
             const int TrainingSize = SeasonalitySize * TrainingSeasons;
@@ -40,52 +43,78 @@ public static void Example()
             for (int i = 0; i < TrainingSeasons; i++)
                 for (int j = 0; j < SeasonalitySize; j++)
                     data.Add(new SsaChangePointData(j));
-            // This is a change point
-            for (int i = 0; i < SeasonalitySize; i++)
-                data.Add(new SsaChangePointData(i * 100));
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
-            // Setup estimator arguments
+            // Setup SsaChangePointDetector arguments
             var inputColumnName = nameof(SsaChangePointData.Value);
             var outputColumnName = nameof(ChangePointPrediction.Prediction);
 
-            // The transformed data.
-            var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
+            // Train the change point detector.
+            ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
-            // Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
-            var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
+            // Create a prediction engine from the model for feeding new data.
+            var engine = model.CreateTimeSeriesPredictionFunction<SsaChangePointData, ChangePointPrediction>(ml);
 
-            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            // Start streaming new data points with no change point to the prediction engine.
+            Console.WriteLine($"Output from ChangePoint predictions on new data:");
             Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
-            int k = 0;
-            foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
-            Console.WriteLine("");
+            ChangePointPrediction prediction = null;
+            for (int i = 0; i < 5; i++)
+            {
+                var value = i;
+                prediction = engine.Predict(new SsaChangePointData(value));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            }
+
+            // Now stream data points that reflect a change in trend.
+            for (int i = 0; i < 5; i++)
+            {
+                var value = (i + 1) * 100;
+                prediction = engine.Predict(new SsaChangePointData(value));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            }
+
+            // Now we demonstrate saving and loading the model.
+
+            // Save the model that exists within the prediction engine.
+            // The engine has been updating this model with every new data point.
+            var modelPath = "model.zip";
+            engine.CheckPoint(ml, modelPath);
+
+            // Load the model.
+            using (var file = File.OpenRead(modelPath))
+                model = ml.Model.Load(file, out DataViewSchema schema);
+
+            // We must create a new prediction engine from the persisted model.
+            engine = model.CreateTimeSeriesPredictionFunction<SsaChangePointData, ChangePointPrediction>(ml);
+
+            // Run predictions on the loaded model.
+            for (int i = 0; i < 5; i++)
+            {
+                var value = (i + 1) * 100;
+                prediction = engine.Predict(new SsaChangePointData(value));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            }
 
-            // Prediction column obtained post-transformation.
+            // Output from ChangePoint predictions on new data:
             // Data    Alert   Score   P-Value Martingale value
-            // 0       0     - 2.53    0.50    0.00
-            // 1       0     - 0.01    0.01    0.00
-            // 2       0       0.76    0.14    0.00
-            // 3       0       0.69    0.28    0.00
-            // 4       0       1.44    0.18    0.00
-            // 0       0     - 1.84    0.17    0.00
-            // 1       0       0.22    0.44    0.00
-            // 2       0       0.20    0.45    0.00
-            // 3       0       0.16    0.47    0.00
-            // 4       0       1.33    0.18    0.00
-            // 0       0     - 1.79    0.07    0.00
-            // 1       0       0.16    0.50    0.00
-            // 2       0       0.09    0.50    0.00
-            // 3       0       0.08    0.45    0.00
-            // 4       0       1.31    0.12    0.00
-            // 0       0     - 1.79    0.07    0.00
-            // 100     1      99.16    0.00    4031.94     <-- alert is on, predicted changepoint
-            // 200     0     185.23    0.00    731260.87
-            // 300     0     270.40    0.01    3578470.47
-            // 400     0     357.11    0.03    45298370.86
+            // 0       0     - 1.01    0.50    0.00
+            // 1       0     - 0.24    0.22    0.00
+            // 2       0     - 0.31    0.30    0.00
+            // 3       0       0.44    0.01    0.00
+            // 4       0       2.16    0.00    0.24
+            // 100     0      86.23    0.00    2076098.24
+            // 200     0     171.38    0.00    809668524.21
+            // 300     1     256.83    0.01    22130423541.93    <-- alert is on, note that delay is expected
+            // 400     0     326.55    0.04    241162710263.29
+            // 500     0     364.82    0.08    597660527041.45   <-- saved to disk
+            // 100     0    - 58.58    0.15    1096021098844.34  <-- loaded from disk and running new predictions
+            // 200     0    - 41.24    0.20    97579154688.98
+            // 300     0    - 30.61    0.24    95319753.87
+            // 400     0      58.87    0.38    14.24
+            // 500     0     219.28    0.36    0.05
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
new file mode 100644
index 0000000000..350e8731ac
--- /dev/null
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
@@ -0,0 +1,90 @@
+using System;
+using System.Collections.Generic;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class DetectChangePointBySsaBatchPrediction
+    {
+        class ChangePointPrediction
+        {
+            [VectorType(4)]
+            public double[] Prediction { get; set; }
+        }
+
+        class SsaChangePointData
+        {
+            public float Value;
+
+            public SsaChangePointData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
+        // The estimator is applied then to identify points where data distribution changed.
+        // This estimator can account for temporal seasonality in the data.
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var ml = new MLContext();
+
+            // Generate sample series data with a recurring pattern and then a change in trend
+            const int SeasonalitySize = 5;
+            const int TrainingSeasons = 3;
+            const int TrainingSize = SeasonalitySize * TrainingSeasons;
+            var data = new List<SsaChangePointData>();
+            for (int i = 0; i < TrainingSeasons; i++)
+                for (int j = 0; j < SeasonalitySize; j++)
+                    data.Add(new SsaChangePointData(j));
+            // This is a change point
+            for (int i = 0; i < SeasonalitySize; i++)
+                data.Add(new SsaChangePointData(i * 100));
+
+            // Convert data to IDataView.
+            var dataView = ml.Data.LoadFromEnumerable(data);
+
+            // Setup estimator arguments
+            var inputColumnName = nameof(SsaChangePointData.Value);
+            var outputColumnName = nameof(ChangePointPrediction.Prediction);
+
+            // The transformed data.
+            var transformedData = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
+
+            // Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
+            var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
+            int k = 0;
+            foreach (var prediction in predictionColumn)
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            Console.WriteLine("");
+
+            // Prediction column obtained post-transformation.
+            // Data    Alert   Score   P-Value Martingale value
+            // 0       0     - 2.53    0.50    0.00
+            // 1       0     - 0.01    0.01    0.00
+            // 2       0       0.76    0.14    0.00
+            // 3       0       0.69    0.28    0.00
+            // 4       0       1.44    0.18    0.00
+            // 0       0     - 1.84    0.17    0.00
+            // 1       0       0.22    0.44    0.00
+            // 2       0       0.20    0.45    0.00
+            // 3       0       0.16    0.47    0.00
+            // 4       0       1.33    0.18    0.00
+            // 0       0     - 1.79    0.07    0.00
+            // 1       0       0.16    0.50    0.00
+            // 2       0       0.09    0.50    0.00
+            // 3       0       0.08    0.45    0.00
+            // 4       0       1.31    0.12    0.00
+            // 0       0     - 1.79    0.07    0.00
+            // 100     1      99.16    0.00    4031.94     <-- alert is on, predicted changepoint
+            // 200     0     185.23    0.00    731260.87
+            // 300     0     270.40    0.01    3578470.47
+            // 400     0     357.11    0.03    45298370.86
+        }
+    }
+}
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
index 268c28238a..8d174b8abb 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
@@ -4,8 +4,10 @@
 
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -47,25 +49,63 @@ public static void Example()
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
-            // Setup estimator arguments
+            // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(ChangePointPrediction.Prediction);
             string inputColumnName = nameof(IidChangePointData.Value);
 
-            // The transformed data.
-            var transformedData = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
+            // Time Series model.
+            ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
-            // Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
-            var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
+            // Create a time series prediction engine from the model.
+            var engine = model.CreateTimeSeriesPredictionFunction<IidChangePointData, ChangePointPrediction>(ml);
+            for (int index = 0; index < 8; index++)
+            {
+                // Anomaly change point detection.
+                var prediction = engine.Predict(new IidChangePointData(5));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 5, prediction.Prediction[0],
+                    prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            }
+
+            // Change point
+            var changePointPrediction = engine.Predict(new IidChangePointData(7));
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, changePointPrediction.Prediction[0],
+                changePointPrediction.Prediction[1], changePointPrediction.Prediction[2], changePointPrediction.Prediction[3]);
+
+            // Checkpoint the model.
+            var modelPath = "temp.zip";
+            engine.CheckPoint(ml, modelPath);
+
+            // Reference to current time series engine because in the next step "engine" will point to the
+            // checkpointed model being loaded from disk.
+            var timeseries1 = engine;
 
-            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
-            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
-            int k = 0;
-            foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
-            Console.WriteLine("");
+            // Load the model.
+            using (var file = File.OpenRead(modelPath))
+                model = ml.Model.Load(file, out DataViewSchema schema);
+
+            // Create a time series prediction engine from the checkpointed model.
+            engine = model.CreateTimeSeriesPredictionFunction<IidChangePointData, ChangePointPrediction>(ml);
+            for (int index = 0; index < 8; index++)
+            {
+                // Anomaly change point detection.
+                var prediction = engine.Predict(new IidChangePointData(7));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
+                    prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            }
+
+            // Prediction from the original time series engine should match the prediction from 
+            // check pointed model.
+            engine = timeseries1;
+            for (int index = 0; index < 8; index++)
+            {
+                // Anomaly change point detection.
+                var prediction = engine.Predict(new IidChangePointData(7));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
+                    prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            }
 
-            // Prediction column obtained post-transformation.
             // Data Alert      Score   P-Value Martingale value
+            // 5       0       5.00    0.50    0.00       <-- Time Series 1.
             // 5       0       5.00    0.50    0.00
             // 5       0       5.00    0.50    0.00
             // 5       0       5.00    0.50    0.00
@@ -73,9 +113,17 @@ public static void Example()
             // 5       0       5.00    0.50    0.00
             // 5       0       5.00    0.50    0.00
             // 5       0       5.00    0.50    0.00
-            // 5       0       5.00    0.50    0.00
-            // 7       1       7.00    0.00    10298.67   <-- alert is on, predicted changepoint
-            // 7       0       7.00    0.13    33950.16
+            // 7       1       7.00    0.00    10298.67   <-- alert is on, predicted changepoint (and model is checkpointed).
+
+            // 7       0       7.00    0.13    33950.16   <-- Time Series 2 : Model loaded back from disk and prediction is made.
+            // 7       0       7.00    0.26    60866.34
+            // 7       0       7.00    0.38    78362.04
+            // 7       0       7.00    0.50    0.01
+            // 7       0       7.00    0.50    0.00
+            // 7       0       7.00    0.50    0.00
+            // 7       0       7.00    0.50    0.00
+
+            // 7       0       7.00    0.13    33950.16   <-- Time Series 1 and prediction is made.
             // 7       0       7.00    0.26    60866.34
             // 7       0       7.00    0.38    78362.04
             // 7       0       7.00    0.50    0.01
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
new file mode 100644
index 0000000000..88893190be
--- /dev/null
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
@@ -0,0 +1,86 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System;
+using System.Collections.Generic;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class DetectIidChangePointBatchPrediction
+    {
+        class ChangePointPrediction
+        {
+            [VectorType(4)]
+            public double[] Prediction { get; set; }
+        }
+
+        class IidChangePointData
+        {
+            public float Value;
+
+            public IidChangePointData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
+        // The estimator is applied then to identify points where data distribution changed.
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var ml = new MLContext();
+
+            // Generate sample series data with a change
+            const int Size = 16;
+            var data = new List<IidChangePointData>(Size);
+            for (int i = 0; i < Size / 2; i++)
+                data.Add(new IidChangePointData(5));
+            // This is a change point
+            for (int i = 0; i < Size / 2; i++)
+                data.Add(new IidChangePointData(7));
+
+            // Convert data to IDataView.
+            var dataView = ml.Data.LoadFromEnumerable(data);
+
+            // Setup estimator arguments
+            string outputColumnName = nameof(ChangePointPrediction.Prediction);
+            string inputColumnName = nameof(IidChangePointData.Value);
+
+            // The transformed data.
+            var transformedData = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
+
+            // Getting the data of the newly created column as an IEnumerable of ChangePointPrediction.
+            var predictionColumn = ml.Data.CreateEnumerable<ChangePointPrediction>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
+            int k = 0;
+            foreach (var prediction in predictionColumn)
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+            Console.WriteLine("");
+
+            // Prediction column obtained post-transformation.
+            // Data Alert      Score   P-Value Martingale value
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 7       1       7.00    0.00    10298.67   <-- alert is on, predicted changepoint
+            // 7       0       7.00    0.13    33950.16
+            // 7       0       7.00    0.26    60866.34
+            // 7       0       7.00    0.38    78362.04
+            // 7       0       7.00    0.50    0.01
+            // 7       0       7.00    0.50    0.00
+            // 7       0       7.00    0.50    0.00
+            // 7       0       7.00    0.50    0.00
+        }
+    }
+}
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
index 862c390ac7..e84ac3ad1b 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
@@ -46,35 +46,55 @@ public static void Example()
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
-            // Setup the estimator arguments
+            // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(IidSpikePrediction.Prediction);
             string inputColumnName = nameof(IidSpikeData.Value);
+            // The transformed model.
+            ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
-            // The transformed data.
-            var transformedData = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
+            // Create a time series prediction engine from the model.
+            var engine = model.CreateTimeSeriesPredictionFunction<IidSpikeData, IidSpikePrediction>(ml);
+            for (int index = 0; index < 5; index++)
+            {
+                // Anomaly spike detection.
+                var prediction = engine.Predict(new IidSpikeData(5));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 5, prediction.Prediction[0],
+                    prediction.Prediction[1], prediction.Prediction[2]);
+            }
+
+            // Spike.
+            var spikePrediction = engine.Predict(new IidSpikeData(10));
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 10, spikePrediction.Prediction[0],
+                spikePrediction.Prediction[1], spikePrediction.Prediction[2]);
 
-            // Getting the data of the newly created column as an IEnumerable of IidSpikePrediction.
-            var predictionColumn = ml.Data.CreateEnumerable<IidSpikePrediction>(transformedData, reuseRowObject: false);
+            // Checkpoint the model.
+            var modelPath = "temp.zip";
+            engine.CheckPoint(ml, modelPath);
 
-            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
-            Console.WriteLine("Alert\tScore\tP-Value");
-            foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1:0.00}\t{2:0.00}", prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
-            Console.WriteLine("");
+            // Load the model.
+            using (var file = File.OpenRead(modelPath))
+                model = ml.Model.Load(file, out DataViewSchema schema);
+
+            for (int index = 0; index < 5; index++)
+            {
+                // Anomaly spike detection.
+                var prediction = engine.Predict(new IidSpikeData(5));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 5, prediction.Prediction[0],
+                    prediction.Prediction[1], prediction.Prediction[2]);
+            }
 
-            // Prediction column obtained post-transformation.
-            // Alert   Score   P-Value
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 1       10.00   0.00   <-- alert is on, predicted spike
-            // 0       5.00    0.26
-            // 0       5.00    0.26
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
+            // Data Alert   Score   P-Value
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 10     1      10.00    0.00  <-- alert is on, predicted spike (check-point model)
+            // 5      0       5.00    0.26  <-- load model from disk.
+            // 5      0       5.00    0.26
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
new file mode 100644
index 0000000000..971e5c741d
--- /dev/null
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
@@ -0,0 +1,77 @@
+using System;
+using System.Collections.Generic;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class DetectIidSpikeBatchPrediction
+    {
+        class IidSpikeData
+        {
+            public float Value;
+
+            public IidSpikeData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        class IidSpikePrediction
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
+        }
+
+        // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
+        // The estimator is applied then to identify spiking points in the series.
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var ml = new MLContext();
+
+            // Generate sample series data with a spike
+            const int Size = 10;
+            var data = new List<IidSpikeData>(Size);
+            for (int i = 0; i < Size / 2; i++)
+                data.Add(new IidSpikeData(5));
+            // This is a spike
+            data.Add(new IidSpikeData(10));
+            for (int i = 0; i < Size / 2; i++)
+                data.Add(new IidSpikeData(5));
+
+            // Convert data to IDataView.
+            var dataView = ml.Data.LoadFromEnumerable(data);
+
+            // Setup the estimator arguments
+            string outputColumnName = nameof(IidSpikePrediction.Prediction);
+            string inputColumnName = nameof(IidSpikeData.Value);
+
+            // The transformed data.
+            var transformedData = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
+
+            // Getting the data of the newly created column as an IEnumerable of IidSpikePrediction.
+            var predictionColumn = ml.Data.CreateEnumerable<IidSpikePrediction>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine("Alert\tScore\tP-Value");
+            foreach (var prediction in predictionColumn)
+                Console.WriteLine("{0}\t{1:0.00}\t{2:0.00}", prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
+            Console.WriteLine("");
+
+            // Prediction column obtained post-transformation.
+            // Alert   Score   P-Value
+            // 0       5.00    0.50
+            // 0       5.00    0.50
+            // 0       5.00    0.50
+            // 0       5.00    0.50
+            // 0       5.00    0.50
+            // 1       10.00   0.00   <-- alert is on, predicted spike
+            // 0       5.00    0.26
+            // 0       5.00    0.26
+            // 0       5.00    0.50
+            // 0       5.00    0.50
+            // 0       5.00    0.50
+        }
+    }
+}
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
index c7293052f2..0381eeaffd 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
@@ -1,7 +1,9 @@
 using System;
 using System.Collections.Generic;
+using System.IO;
 using Microsoft.ML;
 using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.TimeSeries;
 
 namespace Samples.Dynamic
 {
@@ -32,7 +34,7 @@ public static void Example()
             // as well as the source of randomness.
             var ml = new MLContext();
 
-            // Generate sample series data with a recurring pattern and a spike within the pattern
+            // Generate sample series data with a recurring pattern
             const int SeasonalitySize = 5;
             const int TrainingSeasons = 3;
             const int TrainingSize = SeasonalitySize * TrainingSeasons;
@@ -40,54 +42,79 @@ public static void Example()
             for (int i = 0; i < TrainingSeasons; i++)
                 for (int j = 0; j < SeasonalitySize; j++)
                     data.Add(new SsaSpikeData(j));
-            // This is a spike
-            data.Add(new SsaSpikeData(100));
-            for (int i = 0; i < SeasonalitySize; i++)
-                data.Add(new SsaSpikeData(i));
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
-            // Setup estimator arguments
+            // Setup IidSpikeDetector arguments
             var inputColumnName = nameof(SsaSpikeData.Value);
             var outputColumnName = nameof(SsaSpikePrediction.Prediction);
 
-            // The transformed data.
-            var transformedData = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
+            // Train the change point detector.
+            ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
-            // Getting the data of the newly created column as an IEnumerable of SsaSpikePrediction.
-            var predictionColumn = ml.Data.CreateEnumerable<SsaSpikePrediction>(transformedData, reuseRowObject: false);
+            // Create a prediction engine from the model for feeding new data.
+            var engine = model.CreateTimeSeriesPredictionFunction<SsaSpikeData, SsaSpikePrediction>(ml);
 
-            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            // Start streaming new data points with no change point to the prediction engine.
+            Console.WriteLine($"Output from spike predictions on new data:");
             Console.WriteLine("Data\tAlert\tScore\tP-Value");
-            int k = 0;
-            foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
-            Console.WriteLine("");
+            SsaSpikePrediction prediction = null;
+            for (int j = 0; j < 2; j++)
+            {
+                for (int i = 0; i < 5; i++)
+                {
+                    var value = i;
+                    prediction = engine.Predict(new SsaSpikeData(value));
+                    Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
+                }
+            }
+
+            // Now send a data point that reflects a spike.
+            var newValue = 100;
+            prediction = engine.Predict(new SsaSpikeData(newValue));
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", newValue, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
+
+            // Now we demonstrate saving and loading the model.
+
+            // Save the model that exists within the prediction engine.
+            // The engine has been updating this model with every new data point.
+            var modelPath = "model.zip";
+            engine.CheckPoint(ml, modelPath);
+
+            // Load the model.
+            using (var file = File.OpenRead(modelPath))
+                model = ml.Model.Load(file, out DataViewSchema schema);
+
+            // We must create a new prediction engine from the persisted model.
+            engine = model.CreateTimeSeriesPredictionFunction<SsaSpikeData, SsaSpikePrediction>(ml);
+
+            // Run predictions on the loaded model.
+            for (int i = 0; i < 5; i++)
+            {
+                var value = i;
+                prediction = engine.Predict(new SsaSpikeData(value));
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
+            }
 
-            // Prediction column obtained post-transformation.
+            // Output from spike predictions on new data:
             // Data    Alert   Score   P-Value
-            // 0       0     - 2.53    0.50
-            // 1       0     - 0.01    0.01
-            // 2       0       0.76    0.14
-            // 3       0       0.69    0.28
-            // 4       0       1.44    0.18
-            // 0       0     - 1.84    0.17
-            // 1       0       0.22    0.44
-            // 2       0       0.20    0.45
-            // 3       0       0.16    0.47
-            // 4       0       1.33    0.18
-            // 0       0     - 1.79    0.07
-            // 1       0       0.16    0.50
-            // 2       0       0.09    0.50
-            // 3       0       0.08    0.45
-            // 4       0       1.31    0.12
-            // 100     1      98.21    0.00   <-- alert is on, predicted spike
-            // 0       0    - 13.83    0.29
-            // 1       0     - 1.74    0.44
-            // 2       0     - 0.47    0.46
-            // 3       0    - 16.50    0.29
-            // 4       0    - 29.82    0.21
+            // 0       0     - 1.01    0.50
+            // 1       0     - 0.24    0.22
+            // 2       0     - 0.31    0.30
+            // 3       0       0.44    0.01
+            // 4       0       2.16    0.00
+            // 0       0     - 0.78    0.27
+            // 1       0     - 0.80    0.30
+            // 2       0     - 0.84    0.31
+            // 3       0       0.33    0.31
+            // 4       0       2.21    0.07
+            // 100     1      86.17    0.00   <-- alert is on, predicted spike
+            // 0       0     - 2.74    0.40   <-- saved to disk, re-loaded, and running new predictions
+            // 1       0     - 1.47    0.42
+            // 2       0    - 17.50    0.24
+            // 3       0    - 30.82    0.16
+            // 4       0    - 23.24    0.28
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs
new file mode 100644
index 0000000000..c0be801c86
--- /dev/null
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs
@@ -0,0 +1,92 @@
+using System;
+using System.Collections.Generic;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class DetectSpikeBySsaBatchPrediction
+    {
+        class SsaSpikeData
+        {
+            public float Value;
+
+            public SsaSpikeData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        class SsaSpikePrediction
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
+        }
+
+        // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
+        // The estimator is applied then to identify spiking points in the series.
+        // This estimator can account for temporal seasonality in the data.
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var ml = new MLContext();
+
+            // Generate sample series data with a recurring pattern and a spike within the pattern
+            const int SeasonalitySize = 5;
+            const int TrainingSeasons = 3;
+            const int TrainingSize = SeasonalitySize * TrainingSeasons;
+            var data = new List<SsaSpikeData>();
+            for (int i = 0; i < TrainingSeasons; i++)
+                for (int j = 0; j < SeasonalitySize; j++)
+                    data.Add(new SsaSpikeData(j));
+            // This is a spike
+            data.Add(new SsaSpikeData(100));
+            for (int i = 0; i < SeasonalitySize; i++)
+                data.Add(new SsaSpikeData(i));
+
+            // Convert data to IDataView.
+            var dataView = ml.Data.LoadFromEnumerable(data);
+
+            // Setup estimator arguments
+            var inputColumnName = nameof(SsaSpikeData.Value);
+            var outputColumnName = nameof(SsaSpikePrediction.Prediction);
+
+            // The transformed data.
+            var transformedData = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView).Transform(dataView);
+
+            // Getting the data of the newly created column as an IEnumerable of SsaSpikePrediction.
+            var predictionColumn = ml.Data.CreateEnumerable<SsaSpikePrediction>(transformedData, reuseRowObject: false);
+
+            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine("Data\tAlert\tScore\tP-Value");
+            int k = 0;
+            foreach (var prediction in predictionColumn)
+                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
+            Console.WriteLine("");
+
+            // Prediction column obtained post-transformation.
+            // Data    Alert   Score   P-Value
+            // 0       0     - 2.53    0.50
+            // 1       0     - 0.01    0.01
+            // 2       0       0.76    0.14
+            // 3       0       0.69    0.28
+            // 4       0       1.44    0.18
+            // 0       0     - 1.84    0.17
+            // 1       0       0.22    0.44
+            // 2       0       0.20    0.45
+            // 3       0       0.16    0.47
+            // 4       0       1.33    0.18
+            // 0       0     - 1.79    0.07
+            // 1       0       0.16    0.50
+            // 2       0       0.09    0.50
+            // 3       0       0.08    0.45
+            // 4       0       1.31    0.12
+            // 100     1      98.21    0.00   <-- alert is on, predicted spike
+            // 0       0    - 13.83    0.29
+            // 1       0     - 1.74    0.44
+            // 2       0     - 0.47    0.46
+            // 3       0    - 16.50    0.29
+            // 4       0    - 29.82    0.21
+        }
+    }
+}

From 28de467062b6818d6df4e533106c48d052b2bb9f Mon Sep 17 00:00:00 2001
From: Zeeshan Siddiqui <mzs@microsoft.com>
Date: Fri, 5 Apr 2019 09:58:07 -0700
Subject: [PATCH 2/7] PR feedback.

---
 src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs  | 8 ++++----
 src/Microsoft.ML.TimeSeries/PredictionFunction.cs | 7 +++++++
 2 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs b/src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs
index aa64de5495..681d53fe40 100644
--- a/src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs
+++ b/src/Microsoft.ML.TimeSeries/ExtensionsCatalog.cs
@@ -25,7 +25,7 @@ public static class TimeSeriesCatalog
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[DetectIidChangePoint](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs)]
+        /// [!code-csharp[DetectIidChangePoint](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -47,7 +47,7 @@ public static IidChangePointEstimator DetectIidChangePoint(this TransformsCatalo
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[DetectIidSpike](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs)]
+        /// [!code-csharp[DetectIidSpike](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -73,7 +73,7 @@ public static IidSpikeEstimator DetectIidSpike(this TransformsCatalog catalog, s
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[DetectChangePointBySsa](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs)]
+        /// [!code-csharp[DetectChangePointBySsa](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -110,7 +110,7 @@ public static SsaChangePointEstimator DetectChangePointBySsa(this TransformsCata
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[DetectSpikeBySsa](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs)]
+        /// [!code-csharp[DetectSpikeBySsa](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs)]
         /// ]]>
         /// </format>
         /// </example>
diff --git a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
index 070e0c3d10..186d96da7b 100644
--- a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
+++ b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
@@ -65,6 +65,13 @@ public sealed class TimeSeriesPredictionFunction<TSrc, TDst> : PredictionEngineB
         /// </summary>
         /// <param name="env">Usually <see cref="MLContext"/>.</param>
         /// <param name="modelPath">Path to file on disk where the updated model needs to be saved.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        /// [!code-csharp[CheckPoint](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs)]
+        /// ]]>
+        /// </format>
+        /// </example>
         public void CheckPoint(IHostEnvironment env, string modelPath)
         {
             using (var file = File.Create(modelPath))

From c668e075b2e43186f227922ecbd4f4103e6306d7 Mon Sep 17 00:00:00 2001
From: Zeeshan Siddiqui <mzs@microsoft.com>
Date: Tue, 9 Apr 2019 14:39:25 -0700
Subject: [PATCH 3/7] PR feedback.

---
 .../TimeSeries/DetectChangePointBySsaBatchPrediction.cs         | 2 +-
 .../Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs       | 2 ++
 .../Dynamic/Transforms/TimeSeries/DetectIidSpike.cs             | 2 ++
 3 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
index 350e8731ac..a08d4f6493 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Microsoft.Samples.Dynamic
 {
     public static class DetectChangePointBySsaBatchPrediction
     {
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
index 8d174b8abb..284c7fdff9 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
@@ -58,6 +58,8 @@ public static void Example()
 
             // Create a time series prediction engine from the model.
             var engine = model.CreateTimeSeriesPredictionFunction<IidChangePointData, ChangePointPrediction>(ml);
+
+            // Create non-anomalous data and check for change point.
             for (int index = 0; index < 8; index++)
             {
                 // Anomaly change point detection.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
index e84ac3ad1b..da09eeefe4 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
@@ -54,6 +54,8 @@ public static void Example()
 
             // Create a time series prediction engine from the model.
             var engine = model.CreateTimeSeriesPredictionFunction<IidSpikeData, IidSpikePrediction>(ml);
+
+            // Create non-anomalous data and check for anomaly.
             for (int index = 0; index < 5; index++)
             {
                 // Anomaly spike detection.

From f81d115b58c9d76396fde8876a669442fd7fb218 Mon Sep 17 00:00:00 2001
From: Zeeshan Siddiqui <mzs@microsoft.com>
Date: Wed, 10 Apr 2019 13:45:04 -0700
Subject: [PATCH 4/7] PR feedback.

---
 .../TimeSeries/DetectChangePointBySsa.cs      | 116 +++++++++-------
 .../DetectChangePointBySsaBatchPrediction.cs  |  88 +++++++-----
 .../TimeSeries/DetectIidChangePoint.cs        | 127 ++++++++++--------
 .../DetectIidChangePointBatchPrediction.cs    |  70 ++++++----
 .../Transforms/TimeSeries/DetectIidSpike.cs   |  98 ++++++++------
 .../DetectIidSpikeBatchPrediction.cs          |  95 +++++++------
 .../Transforms/TimeSeries/DetectSpikeBySsa.cs | 121 +++++++++--------
 .../DetectSpikeBySsaBatchPrediction.cs        |  99 +++++++++-----
 .../PredictionFunction.cs                     |   4 +-
 9 files changed, 484 insertions(+), 334 deletions(-)

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
index 6ce526e715..66da84080a 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
@@ -9,21 +9,6 @@ namespace Samples.Dynamic
 {
     public static class DetectChangePointBySsa
     {
-        class ChangePointPrediction
-        {
-            [VectorType(4)]
-            public double[] Prediction { get; set; }
-        }
-
-        class SsaChangePointData
-        {
-            public float Value;
-
-            public SsaChangePointData(float value)
-            {
-                Value = value;
-            }
-        }
 
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
@@ -39,42 +24,67 @@ public static void Example()
             const int SeasonalitySize = 5;
             const int TrainingSeasons = 3;
             const int TrainingSize = SeasonalitySize * TrainingSeasons;
-            var data = new List<SsaChangePointData>();
-            for (int i = 0; i < TrainingSeasons; i++)
-                for (int j = 0; j < SeasonalitySize; j++)
-                    data.Add(new SsaChangePointData(j));
+            var data = new List<TimeSeriesData>()
+            {
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup SsaChangePointDetector arguments
-            var inputColumnName = nameof(SsaChangePointData.Value);
+            var inputColumnName = nameof(TimeSeriesData.Value);
             var outputColumnName = nameof(ChangePointPrediction.Prediction);
 
             // Train the change point detector.
             ITransformer model = ml.Transforms.DetectChangePointBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
             // Create a prediction engine from the model for feeding new data.
-            var engine = model.CreateTimeSeriesPredictionFunction<SsaChangePointData, ChangePointPrediction>(ml);
+            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
 
             // Start streaming new data points with no change point to the prediction engine.
             Console.WriteLine($"Output from ChangePoint predictions on new data:");
             Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
-            ChangePointPrediction prediction = null;
+            
+            // Output from ChangePoint predictions on new data:
+            // Data    Alert   Score   P-Value Martingale value
+
             for (int i = 0; i < 5; i++)
-            {
-                var value = i;
-                prediction = engine.Predict(new SsaChangePointData(value));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
-            }
+                PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));
+
+            // 0       0      -1.01    0.50    0.00
+            // 1       0      -0.24    0.22    0.00
+            // 2       0      -0.31    0.30    0.00
+            // 3       0       0.44    0.01    0.00
+            // 4       0       2.16    0.00    0.24
 
             // Now stream data points that reflect a change in trend.
             for (int i = 0; i < 5; i++)
             {
-                var value = (i + 1) * 100;
-                prediction = engine.Predict(new SsaChangePointData(value));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+                int value = (i + 1) * 100;
+                PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
             }
+            // 100     0      86.23    0.00    2076098.24
+            // 200     0     171.38    0.00    809668524.21
+            // 300     1     256.83    0.01    22130423541.93    <-- alert is on, note that delay is expected
+            // 400     0     326.55    0.04    241162710263.29
+            // 500     0     364.82    0.08    597660527041.45   <-- saved to disk
 
             // Now we demonstrate saving and loading the model.
 
@@ -88,33 +98,41 @@ public static void Example()
                 model = ml.Model.Load(file, out DataViewSchema schema);
 
             // We must create a new prediction engine from the persisted model.
-            engine = model.CreateTimeSeriesPredictionFunction<SsaChangePointData, ChangePointPrediction>(ml);
+            engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
 
             // Run predictions on the loaded model.
             for (int i = 0; i < 5; i++)
             {
-                var value = (i + 1) * 100;
-                prediction = engine.Predict(new SsaChangePointData(value));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+                int value = (i + 1) * 100;
+                PrintPrediction(value, engine.Predict(new TimeSeriesData(value)));
             }
 
-            // Output from ChangePoint predictions on new data:
-            // Data    Alert   Score   P-Value Martingale value
-            // 0       0     - 1.01    0.50    0.00
-            // 1       0     - 0.24    0.22    0.00
-            // 2       0     - 0.31    0.30    0.00
-            // 3       0       0.44    0.01    0.00
-            // 4       0       2.16    0.00    0.24
-            // 100     0      86.23    0.00    2076098.24
-            // 200     0     171.38    0.00    809668524.21
-            // 300     1     256.83    0.01    22130423541.93    <-- alert is on, note that delay is expected
-            // 400     0     326.55    0.04    241162710263.29
-            // 500     0     364.82    0.08    597660527041.45   <-- saved to disk
-            // 100     0    - 58.58    0.15    1096021098844.34  <-- loaded from disk and running new predictions
-            // 200     0    - 41.24    0.20    97579154688.98
-            // 300     0    - 30.61    0.24    95319753.87
+            // 100     0     -58.58    0.15    1096021098844.34  <-- loaded from disk and running new predictions
+            // 200     0     -41.24    0.20    97579154688.98
+            // 300     0     -30.61    0.24    95319753.87
             // 400     0      58.87    0.38    14.24
             // 500     0     219.28    0.36    0.05
+
+        }
+
+        private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
+                prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+
+        class ChangePointPrediction
+        {
+            [VectorType(4)]
+            public double[] Prediction { get; set; }
+        }
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
index a08d4f6493..2e0df19007 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsaBatchPrediction.cs
@@ -1,27 +1,12 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class DetectChangePointBySsaBatchPrediction
     {
-        class ChangePointPrediction
-        {
-            [VectorType(4)]
-            public double[] Prediction { get; set; }
-        }
-
-        class SsaChangePointData
-        {
-            public float Value;
-
-            public SsaChangePointData(float value)
-            {
-                Value = value;
-            }
-        }
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify points where data distribution changed.
         // This estimator can account for temporal seasonality in the data.
@@ -35,19 +20,39 @@ public static void Example()
             const int SeasonalitySize = 5;
             const int TrainingSeasons = 3;
             const int TrainingSize = SeasonalitySize * TrainingSeasons;
-            var data = new List<SsaChangePointData>();
-            for (int i = 0; i < TrainingSeasons; i++)
-                for (int j = 0; j < SeasonalitySize; j++)
-                    data.Add(new SsaChangePointData(j));
-            // This is a change point
-            for (int i = 0; i < SeasonalitySize; i++)
-                data.Add(new SsaChangePointData(i * 100));
+            var data = new List<TimeSeriesData>()
+            {
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                //This is a change point
+                new TimeSeriesData(0),
+                new TimeSeriesData(100),
+                new TimeSeriesData(200),
+                new TimeSeriesData(300),
+                new TimeSeriesData(400),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup estimator arguments
-            var inputColumnName = nameof(SsaChangePointData.Value);
+            var inputColumnName = nameof(TimeSeriesData.Value);
             var outputColumnName = nameof(ChangePointPrediction.Prediction);
 
             // The transformed data.
@@ -60,31 +65,50 @@ public static void Example()
             Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
             int k = 0;
             foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
-            Console.WriteLine("");
+                PrintPrediction(data[k++].Value, prediction);
 
             // Prediction column obtained post-transformation.
             // Data    Alert   Score   P-Value Martingale value
-            // 0       0     - 2.53    0.50    0.00
-            // 1       0     - 0.01    0.01    0.00
+            // 0       0      -2.53    0.50    0.00
+            // 1       0      -0.01    0.01    0.00
             // 2       0       0.76    0.14    0.00
             // 3       0       0.69    0.28    0.00
             // 4       0       1.44    0.18    0.00
-            // 0       0     - 1.84    0.17    0.00
+            // 0       0      -1.84    0.17    0.00
             // 1       0       0.22    0.44    0.00
             // 2       0       0.20    0.45    0.00
             // 3       0       0.16    0.47    0.00
             // 4       0       1.33    0.18    0.00
-            // 0       0     - 1.79    0.07    0.00
+            // 0       0      -1.79    0.07    0.00
             // 1       0       0.16    0.50    0.00
             // 2       0       0.09    0.50    0.00
             // 3       0       0.08    0.45    0.00
             // 4       0       1.31    0.12    0.00
-            // 0       0     - 1.79    0.07    0.00
+            // 0       0      -1.79    0.07    0.00
             // 100     1      99.16    0.00    4031.94     <-- alert is on, predicted changepoint
             // 200     0     185.23    0.00    731260.87
             // 300     0     270.40    0.01    3578470.47
             // 400     0     357.11    0.03    45298370.86
         }
+
+        private static void PrintPrediction(float value, ChangePointPrediction prediction) => 
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], 
+                prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+
+        class ChangePointPrediction
+        {
+            [VectorType(4)]
+            public double[] Prediction { get; set; }
+        }
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
index 284c7fdff9..e7292dbb3c 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
@@ -13,21 +13,6 @@ namespace Samples.Dynamic
 {
     public static class DetectIidChangePoint
     {
-        class ChangePointPrediction
-        {
-            [VectorType(4)]
-            public double[] Prediction { get; set; }
-        }
-
-        class IidChangePointData
-        {
-            public float Value;
-
-            public IidChangePointData(float value)
-            {
-                Value = value;
-            }
-        }
 
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify points where data distribution changed.
@@ -39,39 +24,66 @@ public static void Example()
 
             // Generate sample series data with a change
             const int Size = 16;
-            var data = new List<IidChangePointData>(Size);
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidChangePointData(5));
-            // This is a change point
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidChangePointData(7));
+            var data = new List<TimeSeriesData>(Size)
+            {
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+
+                //Change point data.
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(ChangePointPrediction.Prediction);
-            string inputColumnName = nameof(IidChangePointData.Value);
+            string inputColumnName = nameof(TimeSeriesData.Value);
 
             // Time Series model.
             ITransformer model = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView);
 
             // Create a time series prediction engine from the model.
-            var engine = model.CreateTimeSeriesPredictionFunction<IidChangePointData, ChangePointPrediction>(ml);
+            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
 
+            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
+            
+            // Data Alert      Score   P-Value Martingale value
+            
             // Create non-anomalous data and check for change point.
             for (int index = 0; index < 8; index++)
             {
                 // Anomaly change point detection.
-                var prediction = engine.Predict(new IidChangePointData(5));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 5, prediction.Prediction[0],
-                    prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+                PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
             }
 
+            // 5       0       5.00    0.50    0.00       <-- Time Series 1.
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+            // 5       0       5.00    0.50    0.00
+
             // Change point
-            var changePointPrediction = engine.Predict(new IidChangePointData(7));
-            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, changePointPrediction.Prediction[0],
-                changePointPrediction.Prediction[1], changePointPrediction.Prediction[2], changePointPrediction.Prediction[3]);
+            PrintPrediction(7, engine.Predict(new TimeSeriesData(7)));
+
+            // 7       1       7.00    0.00    10298.67   <-- alert is on, predicted changepoint (and model is checkpointed).
 
             // Checkpoint the model.
             var modelPath = "temp.zip";
@@ -86,37 +98,13 @@ public static void Example()
                 model = ml.Model.Load(file, out DataViewSchema schema);
 
             // Create a time series prediction engine from the checkpointed model.
-            engine = model.CreateTimeSeriesPredictionFunction<IidChangePointData, ChangePointPrediction>(ml);
-            for (int index = 0; index < 8; index++)
-            {
-                // Anomaly change point detection.
-                var prediction = engine.Predict(new IidChangePointData(7));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
-                    prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
-            }
-
-            // Prediction from the original time series engine should match the prediction from 
-            // check pointed model.
-            engine = timeseries1;
+            engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, ChangePointPrediction>(ml);
             for (int index = 0; index < 8; index++)
             {
                 // Anomaly change point detection.
-                var prediction = engine.Predict(new IidChangePointData(7));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", 7, prediction.Prediction[0],
-                    prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+                PrintPrediction(7, engine.Predict(new TimeSeriesData(7)));
             }
 
-            // Data Alert      Score   P-Value Martingale value
-            // 5       0       5.00    0.50    0.00       <-- Time Series 1.
-            // 5       0       5.00    0.50    0.00
-            // 5       0       5.00    0.50    0.00
-            // 5       0       5.00    0.50    0.00
-            // 5       0       5.00    0.50    0.00
-            // 5       0       5.00    0.50    0.00
-            // 5       0       5.00    0.50    0.00
-            // 5       0       5.00    0.50    0.00
-            // 7       1       7.00    0.00    10298.67   <-- alert is on, predicted changepoint (and model is checkpointed).
-
             // 7       0       7.00    0.13    33950.16   <-- Time Series 2 : Model loaded back from disk and prediction is made.
             // 7       0       7.00    0.26    60866.34
             // 7       0       7.00    0.38    78362.04
@@ -125,6 +113,15 @@ public static void Example()
             // 7       0       7.00    0.50    0.00
             // 7       0       7.00    0.50    0.00
 
+            // Prediction from the original time series engine should match the prediction from 
+            // check pointed model.
+            engine = timeseries1;
+            for (int index = 0; index < 8; index++)
+            {
+                // Anomaly change point detection.
+                PrintPrediction(7, engine.Predict(new TimeSeriesData(7)));
+            }
+
             // 7       0       7.00    0.13    33950.16   <-- Time Series 1 and prediction is made.
             // 7       0       7.00    0.26    60866.34
             // 7       0       7.00    0.38    78362.04
@@ -133,5 +130,25 @@ public static void Example()
             // 7       0       7.00    0.50    0.00
             // 7       0       7.00    0.50    0.00
         }
+
+        private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0],
+                prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+
+        class ChangePointPrediction
+        {
+            [VectorType(4)]
+            public double[] Prediction { get; set; }
+        }
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
index 88893190be..477662d06a 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
@@ -4,27 +4,13 @@
 
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class DetectIidChangePointBatchPrediction
     {
-        class ChangePointPrediction
-        {
-            [VectorType(4)]
-            public double[] Prediction { get; set; }
-        }
-
-        class IidChangePointData
-        {
-            public float Value;
-
-            public IidChangePointData(float value)
-            {
-                Value = value;
-            }
-        }
 
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify points where data distribution changed.
@@ -36,19 +22,34 @@ public static void Example()
 
             // Generate sample series data with a change
             const int Size = 16;
-            var data = new List<IidChangePointData>(Size);
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidChangePointData(5));
-            // This is a change point
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidChangePointData(7));
+            var data = new List<TimeSeriesData>(Size)
+            {
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+
+                //Change point data.
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+                new TimeSeriesData(7),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup estimator arguments
             string outputColumnName = nameof(ChangePointPrediction.Prediction);
-            string inputColumnName = nameof(IidChangePointData.Value);
+            string inputColumnName = nameof(TimeSeriesData.Value);
 
             // The transformed data.
             var transformedData = ml.Transforms.DetectIidChangePoint(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
@@ -60,8 +61,7 @@ public static void Example()
             Console.WriteLine("Data\tAlert\tScore\tP-Value\tMartingale value");
             int k = 0;
             foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
-            Console.WriteLine("");
+                PrintPrediction(data[k++].Value, prediction);
 
             // Prediction column obtained post-transformation.
             // Data Alert      Score   P-Value Martingale value
@@ -82,5 +82,25 @@ public static void Example()
             // 7       0       7.00    0.50    0.00
             // 7       0       7.00    0.50    0.00
         }
+
+        private static void PrintPrediction(float value, ChangePointPrediction prediction) =>
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}\t{4:0.00}", value, prediction.Prediction[0], 
+                prediction.Prediction[1], prediction.Prediction[2], prediction.Prediction[3]);
+
+        class ChangePointPrediction
+        {
+            [VectorType(4)]
+            public double[] Prediction { get; set; }
+        }
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
index da09eeefe4..ba1a18b999 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
@@ -9,21 +9,6 @@ namespace Samples.Dynamic
 {
     public static class DetectIidSpike
     {
-        class IidSpikeData
-        {
-            public float Value;
-
-            public IidSpikeData(float value)
-            {
-                Value = value;
-            }
-        }
-
-        class IidSpikePrediction
-        {
-            [VectorType(3)]
-            public double[] Prediction { get; set; }
-        }
 
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify spiking points in the series.
@@ -35,39 +20,60 @@ public static void Example()
 
             // Generate sample series data with a spike
             const int Size = 10;
-            var data = new List<IidSpikeData>(Size);
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidSpikeData(5));
-            // This is a spike
-            data.Add(new IidSpikeData(10));
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidSpikeData(5));
+            var data = new List<TimeSeriesData>(Size + 1)
+            {
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+
+                // This is a spike.
+                new TimeSeriesData(10),
+
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
             string outputColumnName = nameof(IidSpikePrediction.Prediction);
-            string inputColumnName = nameof(IidSpikeData.Value);
+            string inputColumnName = nameof(TimeSeriesData.Value);
+
             // The transformed model.
             ITransformer model = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size).Fit(dataView);
 
             // Create a time series prediction engine from the model.
-            var engine = model.CreateTimeSeriesPredictionFunction<IidSpikeData, IidSpikePrediction>(ml);
+            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, IidSpikePrediction>(ml);
+
+            Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
+            Console.WriteLine("Data\tAlert\tScore\tP-Value");
+            
+            // Prediction column obtained post-transformation.
+            // Data Alert   Score   P-Value
 
             // Create non-anomalous data and check for anomaly.
             for (int index = 0; index < 5; index++)
             {
                 // Anomaly spike detection.
-                var prediction = engine.Predict(new IidSpikeData(5));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 5, prediction.Prediction[0],
-                    prediction.Prediction[1], prediction.Prediction[2]);
+                PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
             }
 
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+            // 5      0       5.00    0.50
+
             // Spike.
-            var spikePrediction = engine.Predict(new IidSpikeData(10));
-            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 10, spikePrediction.Prediction[0],
-                spikePrediction.Prediction[1], spikePrediction.Prediction[2]);
+            PrintPrediction(10, engine.Predict(new TimeSeriesData(10)));
+            
+            // 10     1      10.00    0.00  <-- alert is on, predicted spike (check-point model)
 
             // Checkpoint the model.
             var modelPath = "temp.zip";
@@ -80,23 +86,35 @@ public static void Example()
             for (int index = 0; index < 5; index++)
             {
                 // Anomaly spike detection.
-                var prediction = engine.Predict(new IidSpikeData(5));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", 5, prediction.Prediction[0],
-                    prediction.Prediction[1], prediction.Prediction[2]);
+                PrintPrediction(5, engine.Predict(new TimeSeriesData(5)));
             }
 
-            // Data Alert   Score   P-Value
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 5      0       5.00    0.50
-            // 10     1      10.00    0.00  <-- alert is on, predicted spike (check-point model)
             // 5      0       5.00    0.26  <-- load model from disk.
             // 5      0       5.00    0.26
             // 5      0       5.00    0.50
             // 5      0       5.00    0.50
             // 5      0       5.00    0.50
+
+        }
+
+        private static void PrintPrediction(float value, IidSpikePrediction prediction) => 
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], 
+                prediction.Prediction[1], prediction.Prediction[2]);
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        class IidSpikePrediction
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
index 971e5c741d..725c701c49 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
@@ -1,26 +1,12 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class DetectIidSpikeBatchPrediction
     {
-        class IidSpikeData
-        {
-            public float Value;
-
-            public IidSpikeData(float value)
-            {
-                Value = value;
-            }
-        }
-
-        class IidSpikePrediction
-        {
-            [VectorType(3)]
-            public double[] Prediction { get; set; }
-        }
 
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify spiking points in the series.
@@ -32,20 +18,30 @@ public static void Example()
 
             // Generate sample series data with a spike
             const int Size = 10;
-            var data = new List<IidSpikeData>(Size);
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidSpikeData(5));
-            // This is a spike
-            data.Add(new IidSpikeData(10));
-            for (int i = 0; i < Size / 2; i++)
-                data.Add(new IidSpikeData(5));
+            var data = new List<TimeSeriesData>(Size + 1)
+            {
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+
+                // This is a spike.
+                new TimeSeriesData(10),
+
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+                new TimeSeriesData(5),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup the estimator arguments
             string outputColumnName = nameof(IidSpikePrediction.Prediction);
-            string inputColumnName = nameof(IidSpikeData.Value);
+            string inputColumnName = nameof(TimeSeriesData.Value);
 
             // The transformed data.
             var transformedData = ml.Transforms.DetectIidSpike(outputColumnName, inputColumnName, 95, Size / 4).Fit(dataView).Transform(dataView);
@@ -54,24 +50,45 @@ public static void Example()
             var predictionColumn = ml.Data.CreateEnumerable<IidSpikePrediction>(transformedData, reuseRowObject: false);
 
             Console.WriteLine($"{outputColumnName} column obtained post-transformation.");
-            Console.WriteLine("Alert\tScore\tP-Value");
+            Console.WriteLine("Data\tAlert\tScore\tP-Value");
+
+            int k = 0;
             foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1:0.00}\t{2:0.00}", prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
-            Console.WriteLine("");
+                PrintPrediction(data[k++].Value, prediction);
 
             // Prediction column obtained post-transformation.
-            // Alert   Score   P-Value
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 1       10.00   0.00   <-- alert is on, predicted spike
-            // 0       5.00    0.26
-            // 0       5.00    0.26
-            // 0       5.00    0.50
-            // 0       5.00    0.50
-            // 0       5.00    0.50
+            // Data    Alert   Score P-Value
+            // 5       0       5.00    0.50
+            // 5       0       5.00    0.50
+            // 5       0       5.00    0.50
+            // 5       0       5.00    0.50
+            // 5       0       5.00    0.50
+            // 10      1       10.00   0.00   <-- alert is on, predicted spike
+            // 5       0       5.00    0.26
+            // 5       0       5.00    0.26
+            // 5       0       5.00    0.50
+            // 5       0       5.00    0.50
+            // 5       0       5.00    0.50
+        }
+
+        private static void PrintPrediction(float value, IidSpikePrediction prediction) => 
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], 
+                prediction.Prediction[1], prediction.Prediction[2]);
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        class IidSpikePrediction
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
index 0381eeaffd..d1dc929528 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
@@ -9,21 +9,6 @@ namespace Samples.Dynamic
 {
     public static class DetectSpikeBySsa
     {
-        class SsaSpikeData
-        {
-            public float Value;
-
-            public SsaSpikeData(float value)
-            {
-                Value = value;
-            }
-        }
-
-        class SsaSpikePrediction
-        {
-            [VectorType(3)]
-            public double[] Prediction { get; set; }
-        }
 
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify spiking points in the series.
@@ -38,45 +23,68 @@ public static void Example()
             const int SeasonalitySize = 5;
             const int TrainingSeasons = 3;
             const int TrainingSize = SeasonalitySize * TrainingSeasons;
-            var data = new List<SsaSpikeData>();
-            for (int i = 0; i < TrainingSeasons; i++)
-                for (int j = 0; j < SeasonalitySize; j++)
-                    data.Add(new SsaSpikeData(j));
+            var data = new List<TimeSeriesData>()
+            {
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup IidSpikeDetector arguments
-            var inputColumnName = nameof(SsaSpikeData.Value);
+            var inputColumnName = nameof(TimeSeriesData.Value);
             var outputColumnName = nameof(SsaSpikePrediction.Prediction);
 
             // Train the change point detector.
             ITransformer model = ml.Transforms.DetectSpikeBySsa(outputColumnName, inputColumnName, 95, 8, TrainingSize, SeasonalitySize + 1).Fit(dataView);
 
             // Create a prediction engine from the model for feeding new data.
-            var engine = model.CreateTimeSeriesPredictionFunction<SsaSpikeData, SsaSpikePrediction>(ml);
+            var engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
 
             // Start streaming new data points with no change point to the prediction engine.
             Console.WriteLine($"Output from spike predictions on new data:");
             Console.WriteLine("Data\tAlert\tScore\tP-Value");
-            SsaSpikePrediction prediction = null;
+
+            // Output from spike predictions on new data:
+            // Data    Alert   Score   P-Value
+
             for (int j = 0; j < 2; j++)
-            {
                 for (int i = 0; i < 5; i++)
-                {
-                    var value = i;
-                    prediction = engine.Predict(new SsaSpikeData(value));
-                    Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
-                }
-            }
+                    PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));
+
+            // 0       0      -1.01    0.50
+            // 1       0      -0.24    0.22
+            // 2       0      -0.31    0.30
+            // 3       0       0.44    0.01
+            // 4       0       2.16    0.00
+            // 0       0      -0.78    0.27
+            // 1       0      -0.80    0.30
+            // 2       0      -0.84    0.31
+            // 3       0       0.33    0.31
+            // 4       0       2.21    0.07
 
             // Now send a data point that reflects a spike.
-            var newValue = 100;
-            prediction = engine.Predict(new SsaSpikeData(newValue));
-            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", newValue, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
+            PrintPrediction(100, engine.Predict(new TimeSeriesData(100)));
 
-            // Now we demonstrate saving and loading the model.
+            // 100     1      86.17    0.00   <-- alert is on, predicted spike
 
+            // Now we demonstrate saving and loading the model.
             // Save the model that exists within the prediction engine.
             // The engine has been updating this model with every new data point.
             var modelPath = "model.zip";
@@ -87,34 +95,37 @@ public static void Example()
                 model = ml.Model.Load(file, out DataViewSchema schema);
 
             // We must create a new prediction engine from the persisted model.
-            engine = model.CreateTimeSeriesPredictionFunction<SsaSpikeData, SsaSpikePrediction>(ml);
+            engine = model.CreateTimeSeriesPredictionFunction<TimeSeriesData, SsaSpikePrediction>(ml);
 
             // Run predictions on the loaded model.
             for (int i = 0; i < 5; i++)
+                PrintPrediction(i, engine.Predict(new TimeSeriesData(i)));
+            
+            // 0       0      -2.74    0.40   <-- saved to disk, re-loaded, and running new predictions
+            // 1       0      -1.47    0.42
+            // 2       0     -17.50    0.24
+            // 3       0     -30.82    0.16
+            // 4       0     -23.24    0.28
+        }
+
+        private static void PrintPrediction(float value, SsaSpikePrediction prediction) => 
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], 
+                prediction.Prediction[1], prediction.Prediction[2]);
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
             {
-                var value = i;
-                prediction = engine.Predict(new SsaSpikeData(value));
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
+                Value = value;
             }
+        }
 
-            // Output from spike predictions on new data:
-            // Data    Alert   Score   P-Value
-            // 0       0     - 1.01    0.50
-            // 1       0     - 0.24    0.22
-            // 2       0     - 0.31    0.30
-            // 3       0       0.44    0.01
-            // 4       0       2.16    0.00
-            // 0       0     - 0.78    0.27
-            // 1       0     - 0.80    0.30
-            // 2       0     - 0.84    0.31
-            // 3       0       0.33    0.31
-            // 4       0       2.21    0.07
-            // 100     1      86.17    0.00   <-- alert is on, predicted spike
-            // 0       0     - 2.74    0.40   <-- saved to disk, re-loaded, and running new predictions
-            // 1       0     - 1.47    0.42
-            // 2       0    - 17.50    0.24
-            // 3       0    - 30.82    0.16
-            // 4       0    - 23.24    0.28
+        class SsaSpikePrediction
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
         }
     }
 }
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs
index c0be801c86..cc6a798dcf 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsaBatchPrediction.cs
@@ -1,27 +1,12 @@
 using System;
 using System.Collections.Generic;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Samples.Dynamic
+namespace Samples.Dynamic
 {
     public static class DetectSpikeBySsaBatchPrediction
     {
-        class SsaSpikeData
-        {
-            public float Value;
-
-            public SsaSpikeData(float value)
-            {
-                Value = value;
-            }
-        }
-
-        class SsaSpikePrediction
-        {
-            [VectorType(3)]
-            public double[] Prediction { get; set; }
-        }
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify spiking points in the series.
         // This estimator can account for temporal seasonality in the data.
@@ -35,20 +20,41 @@ public static void Example()
             const int SeasonalitySize = 5;
             const int TrainingSeasons = 3;
             const int TrainingSize = SeasonalitySize * TrainingSeasons;
-            var data = new List<SsaSpikeData>();
-            for (int i = 0; i < TrainingSeasons; i++)
-                for (int j = 0; j < SeasonalitySize; j++)
-                    data.Add(new SsaSpikeData(j));
-            // This is a spike
-            data.Add(new SsaSpikeData(100));
-            for (int i = 0; i < SeasonalitySize; i++)
-                data.Add(new SsaSpikeData(i));
+            var data = new List<TimeSeriesData>()
+            {
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+
+                //This is a spike.
+                new TimeSeriesData(100),
+
+                new TimeSeriesData(0),
+                new TimeSeriesData(1),
+                new TimeSeriesData(2),
+                new TimeSeriesData(3),
+                new TimeSeriesData(4),
+            };
 
             // Convert data to IDataView.
             var dataView = ml.Data.LoadFromEnumerable(data);
 
             // Setup estimator arguments
-            var inputColumnName = nameof(SsaSpikeData.Value);
+            var inputColumnName = nameof(TimeSeriesData.Value);
             var outputColumnName = nameof(SsaSpikePrediction.Prediction);
 
             // The transformed data.
@@ -61,32 +67,51 @@ public static void Example()
             Console.WriteLine("Data\tAlert\tScore\tP-Value");
             int k = 0;
             foreach (var prediction in predictionColumn)
-                Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", data[k++].Value, prediction.Prediction[0], prediction.Prediction[1], prediction.Prediction[2]);
-            Console.WriteLine("");
+                PrintPrediction(data[k++].Value, prediction);
 
             // Prediction column obtained post-transformation.
             // Data    Alert   Score   P-Value
-            // 0       0     - 2.53    0.50
-            // 1       0     - 0.01    0.01
+            // 0       0      -2.53    0.50
+            // 1       0      -0.01    0.01
             // 2       0       0.76    0.14
             // 3       0       0.69    0.28
             // 4       0       1.44    0.18
-            // 0       0     - 1.84    0.17
+            // 0       0      -1.84    0.17
             // 1       0       0.22    0.44
             // 2       0       0.20    0.45
             // 3       0       0.16    0.47
             // 4       0       1.33    0.18
-            // 0       0     - 1.79    0.07
+            // 0       0      -1.79    0.07
             // 1       0       0.16    0.50
             // 2       0       0.09    0.50
             // 3       0       0.08    0.45
             // 4       0       1.31    0.12
             // 100     1      98.21    0.00   <-- alert is on, predicted spike
-            // 0       0    - 13.83    0.29
-            // 1       0     - 1.74    0.44
-            // 2       0     - 0.47    0.46
-            // 3       0    - 16.50    0.29
-            // 4       0    - 29.82    0.21
+            // 0       0     -13.83    0.29
+            // 1       0      -1.74    0.44
+            // 2       0      -0.47    0.46
+            // 3       0     -16.50    0.29
+            // 4       0     -29.82    0.21
+        }
+
+        private static void PrintPrediction(float value, SsaSpikePrediction prediction) => 
+            Console.WriteLine("{0}\t{1}\t{2:0.00}\t{3:0.00}", value, prediction.Prediction[0], 
+                prediction.Prediction[1], prediction.Prediction[2]);
+
+        class TimeSeriesData
+        {
+            public float Value;
+
+            public TimeSeriesData(float value)
+            {
+                Value = value;
+            }
+        }
+
+        class SsaSpikePrediction
+        {
+            [VectorType(3)]
+            public double[] Prediction { get; set; }
         }
     }
 }
diff --git a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
index 186d96da7b..1ec34f25bc 100644
--- a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
+++ b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
@@ -268,8 +268,8 @@ public static class PredictionFunctionExtensions
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/IidSpikeDetectorTransform.cs)]
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/IidChangePointDetectorTransform.cs)]
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.css)]
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs)]
         /// ]]>
         /// </format>
         /// </example>

From cab1b81e2c1e16a493564c3304359d98335a21a8 Mon Sep 17 00:00:00 2001
From: Zeeshan Siddiqui <mzs@microsoft.com>
Date: Wed, 10 Apr 2019 13:48:51 -0700
Subject: [PATCH 5/7] cleanup.

---
 .../Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs      | 1 -
 .../Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs        | 1 -
 .../Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs | 1 -
 .../Dynamic/Transforms/TimeSeries/DetectIidSpike.cs              | 1 -
 .../Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs       | 1 -
 .../Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs            | 1 -
 6 files changed, 6 deletions(-)

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
index 66da84080a..833fdceda7 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs
@@ -9,7 +9,6 @@ namespace Samples.Dynamic
 {
     public static class DetectChangePointBySsa
     {
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // It demostrates stateful prediction engine that updates the state of the model and allows for saving/reloading.
         // The estimator is applied then to identify points where data distribution changed.
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
index e7292dbb3c..2890c4ec57 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs
@@ -13,7 +13,6 @@ namespace Samples.Dynamic
 {
     public static class DetectIidChangePoint
     {
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify points where data distribution changed.
         public static void Example()
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
index 477662d06a..760305df33 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePointBatchPrediction.cs
@@ -11,7 +11,6 @@ namespace Samples.Dynamic
 {
     public static class DetectIidChangePointBatchPrediction
     {
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify points where data distribution changed.
         public static void Example()
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
index ba1a18b999..439006a7ec 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs
@@ -9,7 +9,6 @@ namespace Samples.Dynamic
 {
     public static class DetectIidSpike
     {
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify spiking points in the series.
         public static void Example()
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
index 725c701c49..4145214918 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpikeBatchPrediction.cs
@@ -7,7 +7,6 @@ namespace Samples.Dynamic
 {
     public static class DetectIidSpikeBatchPrediction
     {
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify spiking points in the series.
         public static void Example()
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
index d1dc929528..38094bb672 100644
--- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
+++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs
@@ -9,7 +9,6 @@ namespace Samples.Dynamic
 {
     public static class DetectSpikeBySsa
     {
-
         // This example creates a time series (list of Data with the i-th element corresponding to the i-th time slot). 
         // The estimator is applied then to identify spiking points in the series.
         // This estimator can account for temporal seasonality in the data.

From a98751acd0770d4ee972997e900b00fb770abd59 Mon Sep 17 00:00:00 2001
From: Zeeshan Siddiqui <mzs@microsoft.com>
Date: Wed, 10 Apr 2019 14:48:44 -0700
Subject: [PATCH 6/7] PR feedback.

---
 .../PredictionFunction.cs                       | 17 +++++++++++++++--
 1 file changed, 15 insertions(+), 2 deletions(-)

diff --git a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
index 1ec34f25bc..aeb4bc99d1 100644
--- a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
+++ b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
@@ -68,7 +68,14 @@ public sealed class TimeSeriesPredictionFunction<TSrc, TDst> : PredictionEngineB
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[CheckPoint](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs)]
+        /// This is an example for checkpointing time series that detects change point using Singular Spectrum Analysis (SSA) model.
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs)]
+        /// This is an example for checkpointing time series that detects spike using Singular Spectrum Analysis (SSA) model.
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs)]
+        /// This is an example for checkpointing time series that detects change point in independent and identically distributed(IID) time series.
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs)]
+        /// This is an example for checkpointing time series that detects spike in independent and identically distributed(IID) time series.
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -268,8 +275,14 @@ public static class PredictionFunctionExtensions
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.css)]
+        /// This is an example for detecting change point using Singular Spectrum Analysis (SSA) model.
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs)]
+        /// This is an example for detecting spike using Singular Spectrum Analysis (SSA) model.
         /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs)]
+        /// This is an example for detecting change point in independent and identically distributed(IID) time series.
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs)]
+        /// This is an example for detecting spike in independent and identically distributed(IID) time series.
+        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs)]
         /// ]]>
         /// </format>
         /// </example>

From 45acb3147a89917e812d415b9fd81018d98b88f1 Mon Sep 17 00:00:00 2001
From: Zeeshan Siddiqui <mzs@microsoft.com>
Date: Wed, 10 Apr 2019 16:43:37 -0700
Subject: [PATCH 7/7] cleanup.

---
 src/Microsoft.ML.TimeSeries/PredictionFunction.cs | 12 ------------
 1 file changed, 12 deletions(-)

diff --git a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
index aeb4bc99d1..7978b7142f 100644
--- a/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
+++ b/src/Microsoft.ML.TimeSeries/PredictionFunction.cs
@@ -70,12 +70,6 @@ public sealed class TimeSeriesPredictionFunction<TSrc, TDst> : PredictionEngineB
         /// <![CDATA[
         /// This is an example for checkpointing time series that detects change point using Singular Spectrum Analysis (SSA) model.
         /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs)]
-        /// This is an example for checkpointing time series that detects spike using Singular Spectrum Analysis (SSA) model.
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs)]
-        /// This is an example for checkpointing time series that detects change point in independent and identically distributed(IID) time series.
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs)]
-        /// This is an example for checkpointing time series that detects spike in independent and identically distributed(IID) time series.
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs)]
         /// ]]>
         /// </format>
         /// </example>
@@ -277,12 +271,6 @@ public static class PredictionFunctionExtensions
         /// <![CDATA[
         /// This is an example for detecting change point using Singular Spectrum Analysis (SSA) model.
         /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs)]
-        /// This is an example for detecting spike using Singular Spectrum Analysis (SSA) model.
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs)]
-        /// This is an example for detecting change point in independent and identically distributed(IID) time series.
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs)]
-        /// This is an example for detecting spike in independent and identically distributed(IID) time series.
-        /// [!code-csharp[MF](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs)]
         /// ]]>
         /// </format>
         /// </example>