Summary of Toward Physics-guided Time Series Embedding, by Jiaxi Hu et al.
Toward Physics-guided Time Series Embedding
by Jiaxi Hu, Bowen Zhang, Qingsong Wen, Fugee Tsung, Yuxuan Liang
First submitted to arxiv on: 9 Oct 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 The proposed Embedding Duality Theory enables the mutual transformation of dynamical systems and time series using observation functions and physical reconstruction techniques. The parameterized embedding layer provides a linear estimation of non-linear time series dynamics, allowing for the direct employment of physical reconstruction techniques to acquire data embedding representations. This approach leads to a 10X reduction in parameters, a 3X increase in speed, and maximum performance boosts of 18% in expert, 22% in few-shot, and 53% in zero-shot tasks without hyper-parameter tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to model complex systems and analyze time series data. It’s like finding the hidden patterns in a messy data set. The new method is fast and accurate, using physical rules to make predictions. This can be useful for many applications, from predicting weather patterns to analyzing medical data. |
Keywords
» Artificial intelligence » Embedding » Few shot » Time series » Zero shot
