Summary of Granger Causality Detection with Kolmogorov-arnold Networks, by Hongyu Lin et al.
Granger Causality Detection with Kolmogorov-Arnold Networks
by Hongyu Lin, Mohan Ren, Paolo Barucca, Tomaso Aste
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 paper proposes a novel framework called Granger Causality KAN (GC-KAN) to detect causal relationships in time series data. This approach utilizes Kolmogorov-Arnold networks (KANs) and develops a tailored training methodology specifically designed for Granger causality detection. The authors compare the performance of GC-KAN with multilayer perceptrons (MLP) on both Vector Autoregressive (VAR) models and chaotic Lorenz-96 systems, demonstrating the potential of KANs to outperform MLPs in identifying interpretable Granger causal relationships, particularly in high-dimensional settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to find connections between things that happen at different times. It uses special computer programs called Kolmogorov-Arnold networks (KANs) to look for these connections, which are important in many fields like economics and climate science. The researchers tested their approach on some mathematical models and showed that it can be better than other methods at finding the connections. |
Keywords
» Artificial intelligence » Autoregressive » Time series
