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Open Access

STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting

College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China
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Abstract

Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.

Keywords

time series forecastingmultivariate time seriesspatio-temporal decomposition

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Tsinghua Science and Technology
Volume 29 Issue 4,
August 2024
Pages 1232-1247
DOI: 10.26599/TST.2023.9010105
Cite this article:
Jiang Z, Ning Z, Miao H, et al. STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting. Tsinghua Science and Technology, 2024, 29(4): 1232-1247. https://doi.org/10.26599/TST.2023.9010105

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Received: 01 July 2023
Revised: 14 September 2023
Accepted: 26 September 2023
Published: 09 February 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of theCreative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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