Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918356924235776 |
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| author | Klötergens, Christian Dernedde, Tim Schmidt-Thieme, Lars Yalavarthi, Vijaya Krishna |
| author_facet | Klötergens, Christian Dernedde, Tim Schmidt-Thieme, Lars Yalavarthi, Vijaya Krishna |
| contents | Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While recent advances in vision and time series forecasting have shown that lightweight MLP-based architectures (e.g., MLP-Mixer, TSMixer) can rival attention-based models in both accuracy and efficiency, their applicability to irregular and sparse time series remains unexplored. In this paper, we propose IMTS-Mixer, a novel architecture that adapts the principles of Mixer models to the IMTS setting. IMTS-Mixer introduces two key components: (1) ISCAM, a channel-wise encoder that transforms irregular observations into fixed-size vectors using simple MLPs, and (2) ConTP, a continuous time decoder that supports forecasting at arbitrary time points. In our experiments on established benchmark datasets we show that our model achieves state-of-the- art performance in both forecasting accuracy and inference time, while using fewer parameters compared to baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11816 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting Klötergens, Christian Dernedde, Tim Schmidt-Thieme, Lars Yalavarthi, Vijaya Krishna Machine Learning I.5 Forecasting irregularly sampled multivariate time series with missing values (IMTS) is a fundamental challenge in domains such as healthcare, climate science, and biology. While recent advances in vision and time series forecasting have shown that lightweight MLP-based architectures (e.g., MLP-Mixer, TSMixer) can rival attention-based models in both accuracy and efficiency, their applicability to irregular and sparse time series remains unexplored. In this paper, we propose IMTS-Mixer, a novel architecture that adapts the principles of Mixer models to the IMTS setting. IMTS-Mixer introduces two key components: (1) ISCAM, a channel-wise encoder that transforms irregular observations into fixed-size vectors using simple MLPs, and (2) ConTP, a continuous time decoder that supports forecasting at arbitrary time points. In our experiments on established benchmark datasets we show that our model achieves state-of-the- art performance in both forecasting accuracy and inference time, while using fewer parameters compared to baselines. |
| title | Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting |
| topic | Machine Learning I.5 |
| url | https://arxiv.org/abs/2502.11816 |