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

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-make...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 13; no. 5; p. 883
Main Authors: Cheng, Sheng-Tzong, Lyu, Ya-Jin, Lin, Yi-Hong
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2025
Subjects:
Accuracy
Algorithms
Datasets
Decomposition
feature importance
feature selection
Forecasting
Industry 4.0
Neural networks
Noise control
Scientific visualization
Seasonal variations
Solar radiation
Time series
time series decomposition
time series forecasting
Trends
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math13050883