Summary of Are Kans Effective For Multivariate Time Series Forecasting?, by Xiao Han et al.
Are KANs Effective for Multivariate Time Series Forecasting?
by Xiao Han, Xinfeng Zhang, Yiling Wu, Zhenduo Zhang, Zhe Wu
First submitted to arxiv on: 21 Aug 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 A machine learning-based approach to multivariate time series forecasting, the Multivariate Time Series Forecasting (MTSF) problem, has long been a challenge in the field. Existing methods have made significant advancements, but still struggle with inadequate interpretability. The Kolmogorov-Arnold Network (KAN) has shown promise in this area, but its effectiveness in time series forecasting tasks remains to be seen. This paper aims to evaluate the performance, integrability, efficiency, and interpretability of KANs in MTSF. To do so, it proposes a new model called MMK, which combines the benefits of KANs with excellent performance and symbolic function transformation capabilities. The MMK model consists of a mixture-of-KAN layer that assigns variables to best-matched KAN experts. Experimental results demonstrate that KANs are effective in MTSF, outperforming various baselines on seven datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Multivariate time series forecasting is important for predicting the future based on past data. Researchers have developed many methods to do this, but they’re not easy to understand. The Kolmogorov-Arnold Network (KAN) might be a solution, but we need to test it. This paper looks at how well KANs work in forecasting and tries to make them better by combining multiple KAN layers. It also compares KANs to other methods on several datasets. |
Keywords
» Artificial intelligence » Machine learning » Time series
