NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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cs.LG 2years
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A gated residual KAN framework called Temporal Functional Circuits maps edge functions to input lags, ranks them by activation, and validates faithfulness via interventions showing that learned B-splines add predictive value beyond base activations.
citing papers explorer
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NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
NPMixer improves multivariate time series forecasting accuracy by combining a data-adaptive wavelet decomposition with hierarchical neighboring patch mixing via MLPs and channel mixing on high-frequency components.
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Temporal Functional Circuits: From Spline Plots to Faithful Explanations in KAN Forecasting
A gated residual KAN framework called Temporal Functional Circuits maps edge functions to input lags, ranks them by activation, and validates faithfulness via interventions showing that learned B-splines add predictive value beyond base activations.