Summary of Mambats: Improved Selective State Space Models For Long-term Time Series Forecasting, by Xiuding Cai et al.
MambaTS: Improved Selective State Space Models for Long-term Time Series Forecasting
by Xiuding Cai, Yaoyao Zhu, Xueyao Wang, Yu Yao
First submitted to arxiv on: 26 May 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 research paper proposes improvements to the Mamba model, a selective state space model-based architecture for long-term sequence forecasting (LTSF). The authors identify limitations in current Mamba implementations and introduce four targeted enhancements leading to MambaTS. These updates include variable scan along time, temporal Mamba blocks with dropout, variable permutation training, and variable-aware scan along time. Experimental results on eight public datasets demonstrate that MambaTS achieves state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mamba is a new way of doing something called long-term sequence forecasting. This means trying to predict what will happen in the future based on information from earlier times. Some other models, like Transformers, are good at this too, but they have some problems. Mamba has its own strengths and weaknesses, and this paper tries to make it even better. The authors suggest a few new ideas, like looking at all the variables together and not worrying so much about the order of things. They also add some special tricks to prevent the model from getting too good at fitting the data but not generalizing well. Overall, the results show that Mamba can be made to work even better. |
Keywords
* Artificial intelligence * Dropout
