Summary of Is Mamba Effective For Time Series Forecasting?, by Zihan Wang and Fanheng Kong and Shi Feng and Ming Wang and Xiaocui Yang and Han Zhao and Daling Wang and Yifei Zhang
Is Mamba Effective for Time Series Forecasting?
by Zihan Wang, Fanheng Kong, Shi Feng, Ming Wang, Xiaocui Yang, Han Zhao, Daling Wang, Yifei Zhang
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In the realm of time series forecasting (TSF), models must effectively uncover and extract hidden patterns in historical data to accurately predict future states. Transformer-based models excel in TSF due to their ability to recognize these patterns, but their quadratic complexity hinders deployment in real-world scenarios. Mamba, a selective state space model, has gained traction for its efficient processing of sequence dependencies while maintaining near-linear complexity. We propose Simple-Mamba (S-Mamba), a Mamba-based model for TSF tasks. S-Mamba tokenizes time points via a linear layer, extracts inter-variate correlations using a bidirectional Mamba layer, learns temporal dependencies through a Feed-Forward Network, and generates forecast outcomes via a linear mapping layer. Experiments on 13 public datasets demonstrate that S-Mamba achieves leading performance while maintaining low computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict what the weather will be like tomorrow based on yesterday’s data. That’s time series forecasting! The key is finding patterns in the past data to make accurate predictions. Some models, called Transformers, are really good at this, but they can be slow and expensive to use. A new model called Mamba is faster and more efficient. We created a special version of Mamba, called Simple-Mamba, to predict future events based on historical data. Our tests showed that Simple-Mamba works well and uses less computer power than the original Transformer models. |
Keywords
* Artificial intelligence * Time series * Transformer
