Summary of Hippo-kan: Efficient Kan Model For Time Series Analysis, by Sangjong Lee et al.
HiPPO-KAN: Efficient KAN Model for Time Series Analysis
by SangJong Lee, Jin-Kwang Kim, JunHo Kim, TaeHan Kim, James Lee
First submitted to arxiv on: 19 Oct 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 A novel parameter-efficient model for time series forecasting is introduced by integrating High-order Polynomial Projection (HiPPO) theory into the Kolmogorov-Arnold network (KAN) framework. This HiPPO-KAN model outperforms traditional models, maintaining a constant parameter count while varying window sizes and prediction horizons. The experimental results demonstrate the superiority of HiPPO-KAN in terms of scalability and predictive accuracy compared to KAN. Additionally, the study addresses the lagging problem common in time series forecasting models by modifying the loss function to compute the Mean Squared Error (MSE) directly on the coefficient vectors in the HiPPO domain. The resulting predictions closely follow the actual time series data, offering practical contributions for applications in large-scale time series data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of doing time series forecasting is presented. It combines two existing ideas: High-order Polynomial Projection (HiPPO) and Kolmogorov-Arnold network (KAN). This new method, called HiPPO-KAN, works better than old methods for long sequences without needing more computer power or data storage. The study shows that HiPPO-KAN can handle big changes in the data quickly and accurately. |
Keywords
» Artificial intelligence » Loss function » Mse » Parameter efficient » Time series
