Time Series Project

This repository contains my time series analysis project. In the main notebook, I explore and analyze time series data by performing data cleaning, exploratory analysis, visualization, and forecasting. The goal is to understand patterns in the data and build forecasting models that can predict future trends.
Project Overview

    Data Preparation: Cleaning and preprocessing the dataset.
    Exploratory Data Analysis: Visualizing trends, seasonality, and anomalies in the data.
    Forecasting: Applying time series forecasting methods—using ARIMA, SARIMA, Vector Auto Regression, and LSTM—to predict future values.
    Evaluation: Assessing model performance through various metrics.

Getting Started
Prerequisites

Make sure you have the following installed:

    Python 3.7+
    Jupyter Notebook or JupyterLab

You'll also need these Python libraries:

    pandas
    numpy
    matplotlib
    seaborn

Installation

    Clone the Repository:

git clone https://github.com/allmight05/Time-Series-Project.git

Navigate to the Directory:

cd Time-Series-Project

Install Required Libraries:

If you don't already have them, install the necessary packages with:

    pip install pandas numpy matplotlib seaborn

Running the Project

    Open the Jupyter Notebook:

    jupyter notebook "Time series Project.ipynb"

    Run the Notebook Cells:
    Execute the cells sequentially to see the full analysis, modeling, and forecasting process.

Project Structure

    Time series Project.ipynb: Main notebook that documents the entire process from data preprocessing to model evaluation and forecasting.

Contributing

If you have suggestions or improvements, feel free to fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.
License

This project is licensed under the MIT License.