Summary of Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks, by Abhiroop Bhattacharya et al.
Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks
by Abhiroop Bhattacharya, Nandinee Haq
First submitted to arxiv on: 19 Dec 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 The proposed cross-domain adaptation model aims to enhance energy price forecasting by learning market-invariant representations across different markets during training. The doubly residual N-BEATS network, grounded in the Kolmogorov-Arnold representation theorem, is used for time series forecasting. An adversarial framework generates the cross-domain adaptation model, which shows promising results compared to baseline models when predicting day-ahead electricity prices in a zero-shot fashion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to forecast energy prices by learning market-invariant representations across different markets. This helps improve the accuracy of predictions and makes it possible to adapt to new or unseen markets. The method uses special networks that can handle complex patterns in data, making it a powerful tool for forecasting energy prices. |
Keywords
» Artificial intelligence » Domain adaptation » Time series » Zero shot
