SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting
arXiv:2602.16220v1 Announce Type: new 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 va
arXiv:2602.16220v1 Announce Type: new 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 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.
Executive Summary
The article proposes SEMixer, a lightweight multiscale model for long-term time series forecasting. SEMixer features a Random Attention Mechanism and a Multiscale Progressive Mixing Chain, which enhance patch-level semantics and achieve effective temporal mixing. The model is validated on 10 public datasets and achieves third place in the 2025 CCF AlOps Challenge. The results demonstrate the effectiveness of SEMixer in modeling multiscale patterns and addressing semantic gaps across scales.
Key Points
- ▸ SEMixer is a lightweight multiscale model for long-term time series forecasting
- ▸ The model features a Random Attention Mechanism and a Multiscale Progressive Mixing Chain
- ▸ SEMixer is validated on 10 public datasets and achieves third place in the 2025 CCF AlOps Challenge
Merits
Effectiveness in Modeling Multiscale Patterns
SEMixer's ability to capture diverse time-patch interactions and aggregate them via dropout ensemble enhances its capacity to model multiscale dependencies
Memory Efficiency
The Multiscale Progressive Mixing Chain stacks RAM and MLP-Mixer in a memory-efficient manner, allowing for more effective temporal mixing
Demerits
Limited Generalizability
The model's performance may be limited to specific datasets or domains, and its generalizability to other areas remains to be tested
Computational Complexity
The use of a Random Attention Mechanism and a Multiscale Progressive Mixing Chain may increase computational complexity, potentially limiting the model's scalability
Expert Commentary
SEMixer represents a significant advancement in the field of time series forecasting, particularly in its ability to model multiscale patterns and address semantic gaps across scales. The use of a Random Attention Mechanism and a Multiscale Progressive Mixing Chain allows for more effective temporal mixing and enhances the model's capacity to capture diverse time-patch interactions. However, further research is needed to fully explore the model's generalizability and scalability, as well as its potential applications in various domains.
Recommendations
- ✓ Further testing and validation of SEMixer on diverse datasets to assess its generalizability
- ✓ Exploration of potential applications of SEMixer in various domains, such as financial forecasting and traffic prediction
