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TTS-Norm: Forecasting Tensor Time Series via Multi-Way Normalization

Published:10 August 2023Publication History
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Abstract

Tensor time series (TTS) data, a generalization of one-dimensional time series on a high-dimensional space, is ubiquitous in real-world applications. Compared to modeling time series or multivariate time series, which has received much attention and achieved tremendous progress in recent years, tensor time series has been paid less effort. However, properly coping with the TTS is a much more challenging task, due to its high-dimensional and complex inner structure. In this article, we start by revealing the structure of TTS data from afn statistical view of point. Then, in line with this analysis, we perform Tensor Time Series forecasting via a proposed Multi-way Normalization (TTS-Norm), which effectively disentangles multiple heterogeneous low-dimensional substructures from the original high-dimensional structure. Finally, we design a novel objective function for TTS forecasting, accounting for the numerical heterogeneity among different low-dimensional subspaces of TTS. Extensive experiments on two real-world datasets verify the superior performance of our proposed model.1

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            cover image ACM Transactions on Knowledge Discovery from Data
            ACM Transactions on Knowledge Discovery from Data  Volume 18, Issue 1
            January 2024
            854 pages
            ISSN:1556-4681
            EISSN:1556-472X
            DOI:10.1145/3613504
            Issue’s Table of Contents

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            Publication History

            • Published: 10 August 2023
            • Online AM: 26 June 2023
            • Accepted: 19 June 2023
            • Revised: 15 March 2023
            • Received: 1 November 2022
            Published in tkdd Volume 18, Issue 1

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