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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Vydané v:	Mathematics (Basel) Ročník 13; číslo 5; s. 883
Hlavní autori:	Cheng, Sheng-Tzong, Lyu, Ya-Jin, Lin, Yi-Hong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.03.2025
Predmet:	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
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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.
AbstractList	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.
Audience	Academic
Author	Cheng, Sheng-Tzong Lin, Yi-Hong Lyu, Ya-Jin
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Cites_doi	10.1198/jasa.2011.tm09771 10.1609/aaai.v32i1.11491 10.1145/3447548.3467166 10.3115/v1/D14-1179 10.1023/A:1012487302797 10.1155/2016/9717582 10.1609/aaai.v35i12.17325 10.1162/neco.1997.9.8.1735 10.1016/j.dib.2020.105340 10.1016/j.egypro.2011.03.231 10.1016/j.asoc.2018.01.017 10.1145/2939672.2939778
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References	Zhang (ref_18) 2018; 65 ref_14 Hong (ref_17) 2011; 5 ref_12 ref_1 Benvenuto (ref_9) 2020; 29 ref_3 ref_19 Vaswani (ref_4) 2017; 30 Guyon (ref_8) 2002; 46 Hochreiter (ref_2) 1997; 9 Jiao (ref_10) 2016; 2016 ref_16 ref_15 Lundberg (ref_5) 2017; 30 ref_7 Hyndman (ref_11) 2011; 106 Wu (ref_13) 2021; 34 ref_6
References_xml	– volume: 34 start-page: 22419 year: 2021 ident: ref_13 article-title: Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting publication-title: Adv. Neural Inf. Process. Syst. – volume: 30 start-page: 389 year: 2017 ident: ref_5 article-title: A unified approach to interpreting model predictions publication-title: Adv. Neural Inf. Process. Syst. – volume: 106 start-page: 1513 year: 2011 ident: ref_11 article-title: Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing publication-title: J. Am. Stat. Assoc. doi: 10.1198/jasa.2011.tm09771 – ident: ref_19 doi: 10.1609/aaai.v32i1.11491 – ident: ref_7 doi: 10.1145/3447548.3467166 – volume: 30 start-page: 1 year: 2017 ident: ref_4 article-title: Attention is all you need publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_16 – ident: ref_15 – ident: ref_3 doi: 10.3115/v1/D14-1179 – volume: 46 start-page: 389 year: 2002 ident: ref_8 article-title: Gene Selection for Cancer Classification using Support Vector Machines publication-title: Mach. Learn. doi: 10.1023/A:1012487302797 – ident: ref_14 – volume: 2016 start-page: 9717582 year: 2016 ident: ref_10 article-title: Three revised Kalman filtering models for short-term rail transit passenger flow prediction publication-title: Math. Probl. Eng. doi: 10.1155/2016/9717582 – ident: ref_1 – ident: ref_12 doi: 10.1609/aaai.v35i12.17325 – volume: 9 start-page: 1735 year: 1997 ident: ref_2 article-title: Long Short-Term Memory publication-title: Neural Comput. doi: 10.1162/neco.1997.9.8.1735 – volume: 29 start-page: 105340 year: 2020 ident: ref_9 article-title: Application of the ARIMA model on the COVID-2019 epidemic dataset publication-title: Data Brief doi: 10.1016/j.dib.2020.105340 – volume: 5 start-page: 1333 year: 2011 ident: ref_17 article-title: Decomposition and Forecast for Financial Time Series with High-frequency Based on Empirical Mode Decomposition publication-title: Energy Procedia doi: 10.1016/j.egypro.2011.03.231 – volume: 65 start-page: 478 year: 2018 ident: ref_18 article-title: A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.01.017 – ident: ref_6 doi: 10.1145/2939672.2939778
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SubjectTerms	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
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