Summary of A Mamba Foundation Model For Time Series Forecasting, by Haoyu Ma et al.
A Mamba Foundation Model for Time Series Forecasting
by Haoyu Ma, Yushu Chen, Wenlai Zhao, Jinzhe Yang, Yingsheng Ji, Xinghua Xu, Xiaozhu Liu, Hao Jing, Shengzhuo Liu, Guangwen Yang
First submitted to arxiv on: 5 Nov 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 introduces TSMamba, a linear-complexity foundation model for time series forecasting built on the Mamba architecture. TSMamba captures temporal dependencies through forward and backward Mamba encoders, achieving high prediction accuracy. It employs a two-stage transfer learning process to leverage pretrained Mamba LLMs and reduce reliance on large datasets. The model uses patch-wise autoregressive prediction to optimize the backbone, followed by training a prediction head and refining other components for long-term forecasting. TSMamba also introduces a channel-wise compressed attention module to capture cross-channel dependencies during fine-tuning on specific multivariate datasets. Experiments show that TSMamba’s zero-shot performance is comparable to state-of-the-art time series foundation models, despite using significantly less training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new model called TSMamba that helps predict patterns in things that happen over time, like weather or stock prices. The old models used for this were complicated and got slower as the amount of information they had to process increased. So, the creators made a new model that is faster and better at predicting patterns. They also found a way to make it work with less training data, which makes it more useful in real-world situations where there isn’t much data available. The new model did just as well or even better than other models that are specifically designed for this task. |
Keywords
» Artificial intelligence » Attention » Autoregressive » Fine tuning » Time series » Transfer learning » Zero shot
