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[2602.16220] SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
Summary
The paper presents SEMixer, a novel multiscale model designed for long-term time series forecasting, addressing challenges in modeling temporal dependencies through innovative mechanisms.
Why It Matters
As time series data becomes increasingly prevalent across various domains, effective forecasting models like SEMixer can significantly enhance predictive accuracy. This research contributes to the field of machine learning by proposing a solution to the complexities of multiscale data integration, which is crucial for applications in finance, healthcare, and network management.
Key Takeaways
- SEMixer introduces a Random Attention Mechanism (RAM) for improved time-patch interaction.
- The Multiscale Progressive Mixing Chain (MPMC) enhances memory efficiency and temporal mixing.
- The model demonstrates effectiveness on 10 public datasets and in a competitive challenge.
- SEMixer addresses semantic gaps in multiscale time series data.
- The research emphasizes the importance of integrating diverse temporal dependencies for better forecasting.
Computer Science > Machine Learning arXiv:2602.16220 (cs) [Submitted on 18 Feb 2026] Title:SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting Authors:Xu Zhang, Qitong Wang, Peng Wang, Wei Wang View a PDF of the paper titled SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting, by Xu Zhang and 3 other authors View PDF HTML (experimental) Abstract:Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent scales, make the efficient alignment and integration of multi-scale temporal dependencies challenging. To address this, we propose SEMixer, a lightweight multiscale model designed for long-term TSF. SEMixer features two key components: a Random Attention Mechanism (RAM) and a Multiscale Progressive Mixing Chain (MPMC). RAM captures diverse time-patch interactions during training and aggregates them via dropout ensemble at inference, enhancing patch-level semantics and enabling MLP-Mixer to better model multi-scale dependencies. MPMC further stacks RAM and MLP-Mixer in a memory-efficient manner, achieving more effective temporal mixing. It addresses semantic gaps across scales and facilitates better multiscale modeling and forecasting performance. We not only validate the effectiveness of SEMixer on 10 public datasets, but also on the \textit{2025 CCF AlOps Challeng...
