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原文摘要

Functional random forests built on a single functional principal component decomposition of the whole domain may dilute features confined to short intervals, whose location is rarely known in advance. We propose two random forests based on window-specific functional principal components. The global-local functional random forest (GL-FRF) is a late-fusion method that stacks a tuned full-curve forest and one forest per window with penalised convex weights; it contains the full-curve forest as a feasible solution, and its weights indicate which parts of the domain drive the prediction. The window-concatenated functional random forest (WC-FRF) is an early-fusion method that trains a single tuned forest on the concatenated window-specific scores. On twenty UCR datasets, GL-FRF improved on a tuned full-curve forest with the same number of trees on 16, with Brier score reductions of up to 0.21, significantly after Holm adjustment. No method was best on more than six datasets. WC-FRF was more accurate than GL-FRF on 13 datasets but failed when the signal required the full curve, whereas GL-FRF had the smallest mean and maximum excess over the best method across all 32 simulated and real settings, which makes it the safer choice when the location of the signal is unknown.