mlsanityData to 7 Oct 2026

Are KANs Effective for Multivariate Time Series Forecasting?

arXiv 2408.11306 ↗First posted 21 Aug 2024cs.LG cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

Multivariate time series forecasting is a crucial task that predicts the future states based on historical inputs. Related techniques have been developing in parallel with the machine learning community, from early statistical learning methods to current deep learning methods. Despite their significant advancements, existing methods continue to struggle with the challenge of inadequate interpretability. The rise of the Kolmogorov-Arnold Network (KAN) provides a new perspective to solve this challenge, but current work has not yet concluded whether KAN is effective in time series forecasting tasks. In this paper, we aim to evaluate the effectiveness of KANs in time-series forecasting from the perspectives of performance, integrability, efficiency, and interpretability. To this end, we propose the Multi-layer Mixture-of-KAN network (MMK), which achieves excellent performance while retaining KAN's ability to be transformed into a combination of symbolic functions. The core module of MMK is the mixture-of-KAN layer, which uses a mixture-of-experts structure to assign variables to best-matched KAN experts. Then, we explore some useful experimental strategies to deal with the issues in the training stage. Finally, we compare MMK and various baselines on seven datasets. Extensive experimental and visualization results demonstrate that KANs are effective in multivariate time series forecasting. Code is available at: https://github.com/2448845600/EasyTSF.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 21 Aug 2024.

Builds on

27 of 41 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  3. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,413
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  5. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
  6. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  7. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  8. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  9. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  10. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
  11. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 748
  12. 2024KAN: Kolmogorov-Arnold Networkscited by 634
15 more
  1. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  2. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  3. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 327
  4. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
  5. 2019Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directionscited by 172
  6. 2024Wav-KAN: Wavelet Kolmogorov-Arnold Networkscited by 118
  7. 2024Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximationcited by 115
  8. 2024Pathformer: Multi-scale Transformers with Adaptive Pathways for Time Series Forecastingcited by 102
  9. 2024Kolmogorov-Arnold Networks (KANs) for Time Series Analysiscited by 97
  10. 2024KAN or MLP: A Fairer Comparisoncited by 71
  11. 2024fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionscited by 60
  12. 2023SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecastingcited by 53
  13. 2024A Temporal Kolmogorov-Arnold Transformer for Time Series Forecastingcited by 50
  14. 2023Using Sequences of Life-events to Predict Human Livescited by 27
  15. 2023GBT: Two-stage transformer framework for non-stationary time series forecastingcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2025KANMixer: a minimal KAN-centered mixer for long-term time series forecastingcited by 3
  2. 2025KAN vs LSTM Performance in Time Series Forecastingcited by 1
  3. 2024On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networkscited by 0
  4. 2024A KAN-based Interpretable Framework for Process-Informed Prediction of Global Warming Potentialcited by 0
  5. 2025LWFNet: Coherent Doppler Wind Lidar-Based Network for Wind Field Retrievalcited by 0
  6. 2026Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecastingcited by 0

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  3. 2025Time Tracker: Mixture-of-Experts-Enhanced Foundation Time Series Forecasting Model with Decoupled Training Pipelines
  4. 2023Mixture-of-Linear-Experts for Long-term Time Series Forecasting
  5. 2026MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
  6. 2025N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
  7. 2025MoGU: Mixture-of-Gaussians with Uncertainty-based Gating for Time Series Forecasting
  8. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts