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[2502.11816] Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
Summary
This paper introduces IMTS-Mixer, a novel architecture for forecasting irregular multivariate time series with missing values, achieving state-of-the-art performance in accuracy and efficiency.
Why It Matters
Accurate forecasting of irregular multivariate time series is crucial in various fields, including healthcare and climate science. This research addresses a significant gap by applying lightweight MLP-based architectures to this challenge, potentially improving predictive capabilities in critical applications.
Key Takeaways
- IMTS-Mixer adapts Mixer models for irregular multivariate time series forecasting.
- Introduces ISCAM and ConTP components for improved data handling and forecasting.
- Achieves superior performance in accuracy and efficiency compared to existing models.
Computer Science > Machine Learning arXiv:2502.11816 (cs) [Submitted on 17 Feb 2025 (v1), last revised 26 Feb 2026 (this version, v3)] Title:Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting Authors:Christian Klötergens, Tim Dernedde, Lars Schmidt-Thieme, Vijaya Krishna Yalavarthi View a PDF of the paper titled Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting, by Christian Kl\"otergens and 3 other authors View PDF HTML (experimental) Abstract: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 accurac...
