Summary of Mamba or Transformer For Time Series Forecasting? Mixture Of Universals (mou) Is All You Need, by Sijia Peng and Yun Xiong and Yangyong Zhu and Zhiqiang Shen
Mamba or Transformer for Time Series Forecasting? Mixture of Universals (MoU) Is All You Need
by Sijia Peng, Yun Xiong, Yangyong Zhu, Zhiqiang Shen
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to time series forecasting, called Mixture of Universals (MoU), which balances short-term and long-term dependencies for accurate predictions. Existing methods focus on modeling long-term dependencies, neglecting the complexities of short-term dynamics. MoU addresses this challenge by introducing two novel designs: Mixture of Feature Extractors (MoF) and Mixture of Architectures (MoA). MoF improves time series patch representations for short-term dependency, while MoA hierarchically integrates different architectures to model long-term dependency from a hybrid perspective. The proposed approach achieves state-of-the-art performance while maintaining relatively low computational costs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series forecasting is important for making accurate predictions about future events. This paper suggests a new way to do this, called Mixture of Universals (MoU). MoU helps by balancing short-term and long-term dependencies in the data. Right now, most methods only focus on the long-term part, which can make them less effective. The new approach has two parts: one that improves how we represent short-term patterns, and another that combines different types of models to get a better understanding of long-term trends. |
Keywords
» Artificial intelligence » Time series
