Multivariate time series forecasting model based on sliding window machine learning

Hexiang Bai; Peiwen Yu; Zhenshan Yang; Ruijia Xing; Zhang Rui

doi:10.1117/12.3045568

30 September 2024 Multivariate time series forecasting model based on sliding window machine learning

Hexiang Bai, Peiwen Yu, Zhenshan Yang, Ruijia Xing, Zhang Rui

Author Affiliations +

Proceedings Volume 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024); 132860Q (2024) https://doi.org/10.1117/12.3045568
Event: Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 2024, Guangzhou, China

Abstract

This paper introduces a multivariate time series forecasting model that combines a sliding window technique with machine learning, incorporating a convolutional neural network to extract spatio-temporal features. This integration boosts predictive accuracy and robustness. Applied to the Jilin credit card installment dataset, our SHRF method improves AUC PRECISION and F1 scores. Validations on the BeijingAirQuality dataset using the SHL model show our model reduces RMSE per time step to 15.18, demonstrating excellent performance in multivariable time series forecasting.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hexiang Bai, Peiwen Yu, Zhenshan Yang, Ruijia Xing, and Zhang Rui "Multivariate time series forecasting model based on sliding window machine learning", Proc. SPIE 13286, Third International Conference on Electronics Technology and Artificial Intelligence (ETAI 2024), 132860Q (30 September 2024); https://doi.org/10.1117/12.3045568

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