AI RESEARCH
NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
arXiv CS.LG
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ArXi:2605.07476v1 Announce Type: new Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables. In this paper, we propose \textbf{N}eighboring \textbf{P}atching \textbf{Mixer} (\textbf{NPMixer}), a hierarchical architecture featuring a Learnable Stationary Wavelet Transform that adaptively learns filter coefficients to decompose signals into trend and detail components in a data-dependent manner.