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Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability

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arxiv 2406.02496 v1 pith:2LJIX3LV submitted 2024-06-04 cs.LG cs.AI

Kunpeng Xu , Lifei Chen , Shengrui Wang This is my paper · ORCID
classification cs.LGcs.AI
keywords timeseriespredictiveforecastinginterpretabilitykolmogorov-arnoldmt-kant-kan
verification ladder T0 review T1 audit T2 compute T3 formal
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Kolmogorov-Arnold Networks (KAN) is a groundbreaking model recently proposed by the MIT team, representing a revolutionary approach with the potential to be a game-changer in the field. This innovative concept has rapidly garnered worldwide interest within the AI community. Inspired by the Kolmogorov-Arnold representation theorem, KAN utilizes spline-parametrized univariate functions in place of traditional linear weights, enabling them to dynamically learn activation patterns and significantly enhancing interpretability. In this paper, we explore the application of KAN to time series forecasting and propose two variants: T-KAN and MT-KAN. T-KAN is designed to detect concept drift within time series and can explain the nonlinear relationships between predictions and previous time steps through symbolic regression, making it highly interpretable in dynamically changing environments. MT-KAN, on the other hand, improves predictive performance by effectively uncovering and leveraging the complex relationships among variables in multivariate time series. Experiments validate the effectiveness of these approaches, demonstrating that T-KAN and MT-KAN significantly outperform traditional methods in time series forecasting tasks, not only enhancing predictive accuracy but also improving model interpretability. This research opens new avenues for adaptive forecasting models, highlighting the potential of KAN as a powerful and interpretable tool in predictive analytics.

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Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A holomorphic Kolmogorov-Arnold network framework for solving elliptic problems on arbitrary 2D domains

    cs.CE 2025-07 conditional novelty 7.0 of 10

    A boundary-only holomorphic KAN framework solves Laplace, Helmholtz, and elasticity problems on simply and multiply connected 2D domains, outperforming standard PINNs in the tested cases.

  2. SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text

    cs.AI 2026-05 conditional novelty 6.0 of 10

    A 24-dataset benchmark for inducing schema graphs from raw text, plus an auditable LLM-based pipeline that reports the highest scores on the benchmark's four schema-similarity metrics.

  3. A Kolmogorov-Arnold Surrogate Model for Chemical Equilibria: Application to Solid Solutions

    cs.LG 2026-03 conditional novelty 6.0 of 10

    Kolmogorov-Arnold networks trained on GEM-Selektor output accurately approximate chemical equilibria for cement and radium-sulfate solid-solution systems, outperforming MLPs on the cement benchmark and cutting evaluat...

  4. QuKAN: A Quantum Circuit Born Machine approach to Quantum Kolmogorov Arnold Networks

    quant-ph 2025-06 conditional novelty 6.0 of 10

    A quantum circuit Born machine can encode B-spline basis functions and trainable coefficients to form hybrid and fully quantum KAN residual functions, demonstrated on toy classification and regression.

  5. Complementary Matrix-Gated QKAN Fast-Weight Programmers for Quantum Dynamics Forecasting

    quant-ph 2026-07 conditional novelty 5.0 of 10

    Complementary Matrix Gating gives QKAN fast-weight programmers coordinate-wise retain/write control and cuts multi-step quantum-dynamics forecast MSE by at least 91.2% versus scalar gates.

  6. On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators

    cs.LG 2025-09 reject novelty 5.0 of 10

    The paper claims spline-parameterized KAN least-squares estimators achieve the minimax univariate regression rate O(n^{-2r/(2r+1)}) for additive and multiplicative KAN structures, independent of dimension.

  7. Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks

    quant-ph 2025-09 reject novelty 5.0 of 10

    QKANs show strong empirical performance on regression, vision, and language tasks, but the claimed exponential parameter reduction is not rigorously established.

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

    cs.LG 2025-06 conditional novelty 5.0 of 10

    TFKAN places Kolmogorov-Arnold Networks directly on FFT coefficients alongside a time-domain KAN branch, improving long-term forecast accuracy on seven benchmark datasets.

  9. "KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding

    cs.CL 2025-05 conditional novelty 5.0 of 10

    Placing a KAN layer between two linear layers improves spoken language understanding accuracy over linear-only baselines on several speech-intent datasets.

  10. STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting

    cs.LG 2026-07 conditional novelty 4.0 of 10

    STKAN inserts Taylor-polynomial KAN token mixers into spatial and temporal mixing blocks and achieves small but consistent gains over strong baselines on three traffic-flow benchmarks and a tie on a fourth.

  11. SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions

    cs.LG 2026-06 conditional novelty 4.0 of 10

    SechKAN combines sech basis functions with a 1D linear projection to build a KAN-style model whose parameter count matches MLPs and which is competitive or better than several KAN variants on tested benchmarks.

  12. Kolmogorov-Arnold Networks-based GRU and LSTM for Loan Default Early Prediction

    cs.LG 2025-07 conditional novelty 4.0 of 10

    A GRU-KAN hybrid predicts loan defaults 3 and 8 months ahead with reported accuracies of 92% and 88% on a balanced Freddie Mac test set, outperforming LSTM, GRU, and attention baselines.

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