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.
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A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.
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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.
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Don't Learn the Shape: Forecasting Periodic Time Series by Rank-1 Decomposition
A frozen average of the last two cycles matches or exceeds eight shape-learning alternatives on 97 GIFT-Eval configurations for periodic time series forecasting.