Advanced Time Series Forecasting

A comprehensive Jupyter notebook covering classical statistical methods, machine learning, deep learning, and transformer-based approaches for time series forecasting. This project also includes interpretability, hyperparameter tuning, and ensembling techniques.

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Key Features

• Missing value handling & outlier detection
• Exploratory analysis with ACF/PACF and decomposition
• Classical models: ARIMA, ETS
• Machine learning models: XGBoost, RandomForest, SVR
• Deep learning models: LSTM, BiLSTM, LSTM+Dense, Autoencoder
• Transformer-based forecasting
• Facebook Prophet and Kats support
• Hyperparameter tuning (Optuna)
• Model stacking and ensembling
• SHAP-based explainability
• Export predictions and visualizations
• Model evaluation & comparison dashboard

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Setup Instructions

# Create environment
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

# Install required packages
pip install -r requirements.txt

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Run Notebook

Use Jupyter or VS Code to open:

Time_series_ML_and_DL_Enhanced.ipynb

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Output

• Forecast plots
• Attention heatmaps
• Exported CSV of predictions
• Evaluation metrics (MAE, RMSE, MAPE)

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Future Enhancements

• Streamlit dashboard
• AutoML integration
• Multivariate forecasting
• Model monitoring pipeline