AI RESEARCH
DecompKAN: Decomposed Patch-KAN for Long-Term Time Series Forecasting
arXiv CS.LG
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ArXi:2604.23968v1 Announce Type: new Accurate time series forecasting in scientific domains such as climate modeling, physiological monitoring, and energy systems benefits from both competitive predictions and model transparency. This work proposes DecompKAN, a lightweight attention-free architecture that combines trend-residual decomposition, channel-wise patching, learned instance normalization, and B-spline Kolmogoro-Arnold Network (KAN) edge functions.