mlsanityData to 14 Oct 2026

TFKAN: Time-Frequency KAN for Long-Term Time Series Forecasting

arXiv 2506.12696 ↗First posted 15 Jun 2025cs.LGcited by 3

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Abstract (arXiv)

Kolmogorov-Arnold Networks (KANs) are highly effective in long-term time series forecasting due to their ability to efficiently represent nonlinear relationships and exhibit local plasticity. However, prior research on KANs has predominantly focused on the time domain, neglecting the potential of the frequency domain. The frequency domain of time series data reveals recurring patterns and periodic behaviors, which complement the temporal information captured in the time domain. To address this gap, we explore the application of KANs in the frequency domain for long-term time series forecasting. By leveraging KANs' adaptive activation functions and their comprehensive representation of signals in the frequency domain, we can more effectively learn global dependencies and periodic patterns. To integrate information from both time and frequency domains, we propose the Time-Frequency KAN (TFKAN). TFKAN employs a dual-branch architecture that independently processes features from each domain, ensuring that the distinct characteristics of each domain are fully utilized without interference. Additionally, to account for the heterogeneity between domains, we introduce a dimension-adjustment strategy that selectively upscales only in the frequency domain, enhancing efficiency while capturing richer frequency information. Experimental results demonstrate that TFKAN consistently outperforms state-of-the-art (SOTA) methods across multiple datasets. The code is available at https://github.com/LcWave/TFKAN.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 46 references that are in the map. First posted on arXiv 15 Jun 2025.

Builds on

33 of 46 references.

References · 33

  1. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  6. 2017Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learningcited by 950
  7. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  8. 2024KAN: Kolmogorov-Arnold Networkscited by 649
  9. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  10. 2022Transformers in Time Series: A Surveycited by 497
  11. 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
  12. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 346
21 more
  1. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 332
  2. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  3. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 240
  4. 2023FITS: Modeling Time Series with 10k Parameterscited by 195
  5. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 172
  6. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 160
  7. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 117
  8. 2024Kolmogorov-Arnold Networks (KANs) for Time Series Analysiscited by 98
  9. 2024TKAN: Temporal Kolmogorov-Arnold Networkscited by 71
  10. 2024Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretabilitycited by 70
  11. 2023MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixingcited by 53
  12. 2024A Temporal Kolmogorov-Arnold Transformer for Time Series Forecastingcited by 52
  13. 2024A Comprehensive Survey of Deep Learning for Time Series Forecasting: Architectural Diversity and Open Challengescited by 47
  14. 2025TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecastingcited by 30
  15. 2023PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformercited by 22
  16. 2023A Systematic Review for Transformer-based Long-term Series Forecastingcited by 10
  17. 2024ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecastingcited by 8
  18. 2025Kolmogorov-Arnold Recurrent Network for Short Term Load Forecasting Across Diverse Consumerscited by 7
  19. 2023A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecastingcited by 5
  20. 2024HiPPO-KAN: Efficient KAN Model for Time Series Analysiscited by 3
  21. 2024Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025KAN vs LSTM Performance in Time Series Forecastingcited by 1
  2. 2026Dualformer: Time-Frequency Dual Domain Learning for Long-term Time Series Forecastingcited by 0
  3. 2026Do Transformers Have the Ability for Periodicity Generalization?cited by 0