Time series forecast intervals using circular bootstrapped training simulation with invariant distance KNN - Srisuradetchai, Patchanok; Kamlangdee,Parattakorn (Zielona Góra: Uniwersytet Zielonogórski)
This paper presents a nonparametric interval forecasting method that combines circular block bootstrap resampling with complexity-invariant K-nearest-neighbor time-series prediction. Prediction intervals are obtained directly from bootstrapresampled training series, thereby preserving temporal dependence while accounting for forecast uncertainty. Under weak dependence and local stability assumptions, asymptotic validity of the resulting prediction intervals is established.
Keywords: k-nearest neighbors; circular block bootstrapping; uncertainty quantification; rolling-window forecasting