Summary of Time-ssm: Simplifying and Unifying State Space Models For Time Series Forecasting, by Jiaxi Hu et al.
Time-SSM: Simplifying and Unifying State Space Models for Time Series Forecasting
by Jiaxi Hu, Disen Lan, Ziyu Zhou, Qingsong Wen, Yuxuan Liang
First submitted to arxiv on: 25 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 paper proposes a novel framework for applying State Space Models (SSMs) in time series forecasting, an area where SSMs have been underexplored despite their potential. The Dynamic Spectral Operator framework provides a more intuitive and general approach to capturing temporal and channel dependencies in time series data. Building on this theory, the authors introduce Time-SSM, a foundation model that uses only one-seventh of the parameters compared to Mamba, another well-known SSM-based model. Experimental results validate both the theoretical framework and the superior performance of Time-SSM. This paper demonstrates the effectiveness of applying SSMs in time series forecasting and has implications for various applications, including modeling and predicting continuous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how to use State Space Models (SSMs) to make better predictions about what will happen next in a sequence of events. SSMs are like special tools that help us understand patterns and trends in data. Right now, these tools aren’t being used as much as they could be for forecasting things like stock prices or weather. The authors of this paper come up with a new way to use SSMs that is more helpful and easier to understand. They also create a new model called Time-SSM that does an even better job than other models at predicting what will happen next. This research could help us make better predictions in all sorts of areas, like business or science. |
Keywords
» Artificial intelligence » Time series
