MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911168437682176 |
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| author | Zhong, Shuhan Zhuo, Weipeng Song, Sizhe Li, Guanyao Yu, Zhongyi Chan, S. -H. Gary |
| author_facet | Zhong, Shuhan Zhuo, Weipeng Song, Sizhe Li, Guanyao Yu, Zhongyi Chan, S. -H. Gary |
| contents | Irregular multivariate time series (IMTS) is characterized by the lack of synchronized observations across its different channels. In this paper, we point out that this channel-wise asynchrony can lead to poor channel-wise modeling of existing deep learning methods. To overcome this limitation, we propose MTM, a multi-scale token mixing transformer for the classification of IMTS. We find that the channel-wise asynchrony can be alleviated by down-sampling the time series to coarser timescales, and propose to incorporate a masked concat pooling in MTM that gradually down-samples IMTS to enhance the channel-wise attention modules. Meanwhile, we propose a novel channel-wise token mixing mechanism which proactively chooses important tokens from one channel and mixes them with other channels, to further boost the channel-wise learning of our model. Through extensive experiments on real-world datasets and comparison with state-of-the-art methods, we demonstrate that MTM consistently achieves the best performance on all the benchmarks, with improvements of up to 3.8% in AUPRC for classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_17809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification Zhong, Shuhan Zhuo, Weipeng Song, Sizhe Li, Guanyao Yu, Zhongyi Chan, S. -H. Gary Machine Learning Irregular multivariate time series (IMTS) is characterized by the lack of synchronized observations across its different channels. In this paper, we point out that this channel-wise asynchrony can lead to poor channel-wise modeling of existing deep learning methods. To overcome this limitation, we propose MTM, a multi-scale token mixing transformer for the classification of IMTS. We find that the channel-wise asynchrony can be alleviated by down-sampling the time series to coarser timescales, and propose to incorporate a masked concat pooling in MTM that gradually down-samples IMTS to enhance the channel-wise attention modules. Meanwhile, we propose a novel channel-wise token mixing mechanism which proactively chooses important tokens from one channel and mixes them with other channels, to further boost the channel-wise learning of our model. Through extensive experiments on real-world datasets and comparison with state-of-the-art methods, we demonstrate that MTM consistently achieves the best performance on all the benchmarks, with improvements of up to 3.8% in AUPRC for classification. |
| title | MTM: A Multi-Scale Token Mixing Transformer for Irregular Multivariate Time Series Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.17809 |