# Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis

arXiv: https://arxiv.org/abs/2408.07314 · HTML: https://mlsanity.com/paper/2408.07314/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 12 of 12)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [The UCR Time Series Archive](https://mlsanity.com/paper/1810.07758/) (2018)
- [InceptionTime: Finding AlexNet for Time Series Classification](https://mlsanity.com/paper/1909.04939/) (2019)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series](https://mlsanity.com/paper/1811.10166/) (2018)
- [Adversarial Attacks on Deep Neural Networks for Time Series Classification](https://mlsanity.com/paper/1903.07054/) (2019)
- [Adversarial Attacks on Time Series](https://mlsanity.com/paper/1902.10755/) (2019)
- [SWAP: Exploiting Second-Ranked Logits for Adversarial Attacks on Time Series](https://mlsanity.com/paper/2309.02752/) (2023)
- [The robust way to stack and bag: the local Lipschitz way](https://mlsanity.com/paper/2206.00513/) (2022)

## Built on by (most cited first, 16 of 16)

- [A Survey on Kolmogorov-Arnold Network](https://mlsanity.com/paper/2411.06078/) (2024)
- [Addressing common misinterpretations of KART and UAT in neural network literature](https://mlsanity.com/paper/2408.16389/) (2024)
- [Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classification](https://mlsanity.com/paper/2411.14904/) (2024)
- [Exploring Adversarial Transferability between Kolmogorov-arnold Networks](https://mlsanity.com/paper/2503.06276/) (2025)
- [Leveraging KANs for Expedient Training of Multichannel MLPs via Preconditioning and Geometric Refinement](https://mlsanity.com/paper/2505.18131/) (2025)
- [iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network](https://mlsanity.com/paper/2504.16432/) (2025)
- [Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction](https://mlsanity.com/paper/2408.08713/) (2024)
- [Multilevel Training for Kolmogorov Arnold Networks](https://mlsanity.com/paper/2603.04827/) (2026)
- [KAN KAN Buff Signed Graph Neural Networks?](https://mlsanity.com/paper/2501.00709/) (2025)
- [TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization](https://mlsanity.com/paper/2503.03276/) (2025)
- [HiPreNets: High-Precision Neural Networks through Progressive Training](https://mlsanity.com/paper/2506.15064/) (2025)
- [A Primer on Kolmogorov-Arnold Networks (KANs) for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2510.16940/) (2025)
- [Air Pollution Forecasting in Bucharest](https://mlsanity.com/paper/2511.00532/) (2025)
- [DecoKAN: Interpretable Decomposition for Forecasting Cryptocurrency Market Dynamics](https://mlsanity.com/paper/2512.20028/) (2025)
- [From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks](https://mlsanity.com/paper/2606.22258/) (2026)
- [KAN-Robust-Bench: A Benchmark for Evaluating the Robustness of Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2608.21488/) (2026)

## Abstract (arXiv)

Kolmogorov-Arnold Networks (KAN) has recently attracted significant attention as a promising alternative to traditional Multi-Layer Perceptrons (MLP). Despite their theoretical appeal, KAN require validation on large-scale benchmark datasets. Time series data, which has become increasingly prevalent in recent years, especially univariate time series are naturally suited for validating KAN. Therefore, we conducted a fair comparison among KAN, MLP, and mixed structures. The results indicate that KAN can achieve performance comparable to, or even slightly better than, MLP across 128 time series datasets. We also performed an ablation study on KAN, revealing that the output is primarily determined by the base component instead of b-spline function. Furthermore, we assessed the robustness of these models and found that KAN and the hybrid structure MLPKAN exhibit significant robustness advantages, attributed to their lower Lipschitz constants. This suggests that KAN and KAN layers hold strong potential to be robust models or to improve the adversarial robustness of other models.