Summary of A Wave Is Worth 100 Words: Investigating Cross-domain Transferability in Time Series, by Xiangkai Ma et al.
A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series
by Xiangkai Ma, Xiaobin Hong, Wenzhong Li, Sanglu Lu
First submitted to arxiv on: 1 Dec 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 cross-domain pretraining method, Wave Quantization for Time Series (WQ4TS), enables advanced time series models to learn temporal pattern knowledge from different domains and migrate across multiple downstream tasks. This novel approach transfers time series data into a common spectral latent space, allowing the model to adapt to zero- and few-shot scenarios without prior knowledge of the dataset. WQ4TS achieves state-of-the-art results in forecasting, imputation, and classification tasks on 87.5% of all tasks, with an average improvement of up to 34.7%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Time series analysis is important for understanding patterns in data. Right now, we have good methods for training models on specific datasets. But it’s hard to use these methods to predict what will happen in new situations. This is because the data from different situations looks very different. The authors of this paper want to solve this problem by creating a way to teach machines about patterns in time series data that can be used across many different situations. They created a new method called Wave Quantization for Time Series (WQ4TS). WQ4TS is like a translator that helps machines understand data from one situation and use it to make predictions in another situation. This means that machines can learn to predict things even when they have very little information. |
Keywords
» Artificial intelligence » Classification » Few shot » Latent space » Pretraining » Quantization » Time series
