A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866913280625213440 |
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| author | Zhong, Shuhan Song, Sizhe Zhuo, Weipeng Li, Guanyao Liu, Yang Chan, S. -H. Gary |
| author_facet | Zhong, Shuhan Song, Sizhe Zhuo, Weipeng Li, Guanyao Liu, Yang Chan, S. -H. Gary |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11959 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis Zhong, Shuhan Song, Sizhe Zhuo, Weipeng Li, Guanyao Liu, Yang Chan, S. -H. Gary Machine Learning Artificial Intelligence 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. |
| title | A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.11959 |