Robust Time Series Forecasting via Time Differencing and Stacking

This paper presents a comparative study of machine learning models for time series forecasting, focusing on time differencing and a novel stacking architecture (SGDRegressor, Polynomial Regressor, and Support Vector Machines).

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Recommended citation: A. Bobde, V. Narnaware, S. Tawari and A. Thomas, "Robust Time Series Forecasting via Time Differencing and Stacking," 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Bangalore, India, 2022, pp. 1-8, doi: 10.1109/SMARTGENCON56628.2022.10083651.
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