STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting
Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing challenges for spatio-temporal forecasting. Existing approaches focus on graph, attention, and decomposition architectures but neglect the underlying nonlinear function approximator. STKAN introduces Taylor-polynomial Kolmogorov-Arnold Network modules into spatial and temporal token mixing. It constructs high-level spatial representations via a learnable soft node-group assignment, applies group-wise spatial mixing, models temporal dependencies, and uses self-attention for long-range interactions. Experiments on five traffic benchmarks show competitive performance, outperforming an MLP-based variant, suggesting that nonlinear function approximator design complements architectural innovation.
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[Submitted on 14 Jul 2026]
Title:STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting
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Abstract:Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting. Existing approaches have developed increasingly sophisticated graph, attention, and decomposition architectures, while the influence of the underlying nonlinear function approximator has received comparatively less attention. In this work, we propose STKAN, a spatio-temporal forecasting architecture that introduces Taylor-polynomial Kolmogorov--Arnold Network modules into spatial and temporal token mixing. STKAN first constructs high-level spatial representations through a learnable soft node-group assignment mechanism, applies group-wise spatial mixing, and subsequently models temporal dependencies over the compressed sequence. Spatial and temporal self-attention layers are further employed to capture long-range interactions. Experiments on five traffic forecasting benchmarks show that STKAN achieves competitive performance and performs better than the evaluated MLP-based variant in the tested settings. These results suggest that the design of nonlinear function approximators can serve as a useful complement to architectural design in spatio-temporal forecasting.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.13108 [cs.LG]
(or arXiv:2607.13108v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.13108
arXiv-issued DOI via DataCite
Submission history
From: Sicong Lai [view email] [v1] Tue, 14 Jul 2026 10:18:54 UTC (3,876 KB)
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