A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis

• URL: http://arxiv.org/abs/2310.11959v2
• Date: Sun, 24 Mar 2024 11:50:28 GMT
• Title: A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
• Authors: Shuhan Zhong, Sizhe Song, Weipeng Zhuo, Guanyao Li, Yang Liu, S. -H. Gary Chan,
• Abstract summary: We propose MSD-Mixer, a Multi-Scale Decomposition-Mixer, which learns to explicitly decompose and represent the input time series in its different layers. We demonstrate that MSD-Mixer consistently and significantly outperforms other state-of-the-art algorithms with better efficiency.
• Score: 14.40202378972828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series data, including univariate and multivariate ones, are characterized by unique composition and complex multi-scale temporal variations. They often require special consideration of decomposition and multi-scale modeling to analyze. Existing deep learning methods on this best fit to univariate time series only, and have not sufficiently considered sub-series modeling and decomposition completeness. To address these challenges, we propose MSD-Mixer, a Multi-Scale Decomposition MLP-Mixer, which learns to explicitly decompose and represent the input time series in its different layers. To handle the multi-scale temporal patterns and multivariate dependencies, we propose a novel temporal patching approach to model the time series as multi-scale patches, and employ MLPs to capture intra- and inter-patch variations and channel-wise correlations. In addition, we propose a novel loss function to constrain both the mean and the autocorrelation of the decomposition residual for better decomposition completeness. Through extensive experiments on various real-world datasets for five common time series analysis tasks, we demonstrate that MSD-Mixer consistently and significantly outperforms other state-of-the-art algorithms with better efficiency.

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