iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908481761574912 |
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| author | Liang, Ziran An, Rui Fan, Wenqi Rao, Yanghui Liang, Yuxuan |
| author_facet | Liang, Ziran An, Rui Fan, Wenqi Rao, Yanghui Liang, Yuxuan |
| contents | As time evolves, data within specific domains exhibit predictability that motivates time series forecasting to predict future trends from historical data. However, current deep forecasting methods can achieve promising performance but generally lack interpretability, hindering trustworthiness and practical deployment in safety-critical applications such as auto-driving and healthcare. In this paper, we propose a novel interpretable model, iTFKAN, for credible time series forecasting. iTFKAN enables further exploration of model decision rationales and underlying data patterns due to its interpretability achieved through model symbolization. Besides, iTFKAN develops two strategies, prior knowledge injection, and time-frequency synergy learning, to effectively guide model learning under complex intertwined time series data. Extensive experimental results demonstrated that iTFKAN can achieve promising forecasting performance while simultaneously possessing high interpretive capabilities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_16432 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network Liang, Ziran An, Rui Fan, Wenqi Rao, Yanghui Liang, Yuxuan Machine Learning Artificial Intelligence As time evolves, data within specific domains exhibit predictability that motivates time series forecasting to predict future trends from historical data. However, current deep forecasting methods can achieve promising performance but generally lack interpretability, hindering trustworthiness and practical deployment in safety-critical applications such as auto-driving and healthcare. In this paper, we propose a novel interpretable model, iTFKAN, for credible time series forecasting. iTFKAN enables further exploration of model decision rationales and underlying data patterns due to its interpretability achieved through model symbolization. Besides, iTFKAN develops two strategies, prior knowledge injection, and time-frequency synergy learning, to effectively guide model learning under complex intertwined time series data. Extensive experimental results demonstrated that iTFKAN can achieve promising forecasting performance while simultaneously possessing high interpretive capabilities. |
| title | iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.16432 |