research-article

Predicting high-dimensional time series data with spatial, temporal and global information

Authors:

Yuren ZhouAuthors Info & Claims

Volume 607, Issue C

Pages 477 - 492

https://doi.org/10.1016/j.ins.2022.06.021

Published: 01 August 2022 Publication History

Abstract

In the field of time series forecasting, deep learning and dynamics-based methods are two main research directions. The former focuses on the temporal information of the data while the latter emphasizes on the spatial information of the data, and rare methods combine the two information properly. In order to make better use of the information in the data, we propose the STSM (spatiotemporal skip-connection model) based on the dynamics framework, which contains a temporal module composed of CNN and a spatial module composed of fully connected layers, as well as a skip connection to the original input to fuse temporal, spatial, and global information in the data. To predict the future value of the target variable, STSM is required to learn a mapping from original attractors to delay attractors in an end-to-end framework. The results of ablation and contrast experiments on one simulated dataset and seven real-world datasets show that STSM not only performs better than a separate temporal or spatial module, but also predicts more accurately than other traditional methods. Besides, we verify the robustness of the model in different scenarios through several experiments.

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Cited By

Abbasimehr HNoshad APaki R(2024)A novel featurization methodology using JaGen algorithm for time series forecasting with deep learning techniquesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121279235:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121279

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Predicting high-dimensional time series data with spatial, temporal and global information

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cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 607, Issue C

Aug 2022

1637 pages

ISSN:0020-0255

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Elsevier Inc.

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Elsevier Science Inc.

United States

Publication History

Published: 01 August 2022

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Cited By

Abbasimehr HNoshad APaki R(2024)A novel featurization methodology using JaGen algorithm for time series forecasting with deep learning techniquesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121279235:COnline publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121279

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