mlsanityData to 14 Oct 2026

SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting

arXiv 2602.16220 ↗First posted 18 Feb 2026cs.LGcited by 4

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Abstract (arXiv)

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 textit2025 CCF AlOps Challenge based on 21GB real wireless network data, where SEMixer achieves third place. The code is available at the link https://github.com/Meteor-Stars/SEMixer.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 18 Feb 2026.

Builds on

10 of 34 references.

References · 10

  1. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  4. 2020Rethinking Attention with Performerscited by 1,601
  5. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,446
  6. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  7. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 248
  8. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 116
  9. 2021REST: Relational Event-driven Stock Trend Forecastingcited by 30
  10. 2017Analysis of dropout learning regarded as ensemble learningcited by 15

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

  1. 2026SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithmscited by 2
  2. 2026Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?cited by 1
  3. 2026Self-Adaptive Scale Handling for Forecasting Time Series with Scale Heterogeneitycited by 0
  4. 2026REATS: LLM Reasoning-based Ensemble Learning for Adaptive Time Series Forecastingcited by 0