Linear Model and Gradient Feature Elimination Algorithm Based on Seasonal Decomposition for Time Series Forecasting

Sheng-Tzong Cheng (), Ya-Jin Lyu and Yi-Hong Lin
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Sheng-Tzong Cheng: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
Ya-Jin Lyu: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
Yi-Hong Lin: Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan

Mathematics, 2025, vol. 13, issue 5, 1-29

Abstract: In the wave of digital transformation and Industry 4.0, accurate time series forecasting has become critical across industries such as manufacturing, energy, and finance. However, while deep learning models offer high predictive accuracy, their lack of interpretability often undermines decision-makers’ trust. This study proposes a linear time series model architecture based on seasonal decomposition. The model effectively captures trends and seasonality using an additive decomposition, chosen based on initial data visualization, indicating stable seasonal variations. An augmented feature generator is introduced to enhance predictive performance by generating features such as differences, rolling statistics, and moving averages. Furthermore, we propose a gradient-based feature importance method to improve interpretability and implement a gradient feature elimination algorithm to reduce noise and enhance model accuracy. The approach is validated on multiple datasets, including order demand, energy load, and solar radiation, demonstrating its applicability to diverse time series forecasting tasks.

Keywords: time series forecasting; time series decomposition; feature selection; feature importance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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