Autor:

Srisuradetchai, Patchanok ; Kamlangdee,Parattakorn

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

Time series forecast intervals using circular bootstrapped training simulation with invariant distance KNN

Tytuł publikacji grupowej:

AMCS, volume 36 (2026)

Temat i słowa kluczowe:

k-nearest neighbors ; circular block bootstrapping ; uncertainty quantification ; rolling-window forecasting

Abstract:

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. ; The proposed method is evaluated using twelve time-series datasets drawn from economic, environmental, industrial, and energy applications. Empirical performance is compared with seasonal autoregressive integrated moving average models and long short-term memory neural networks using the mean absolute percentage error, empirical coverage probability, and interval score. The results show that the proposed approach yields prediction intervals of moderate width with competitive forecasting accuracy across most datasets, while empirical coverage remains close to the nominal level. Mild undercoverage is observed in short samples, attributable to limited data availability and fixed tuning parameters.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2026

Typ zasobu:

artykuł

DOI:

10.61822/amcs-2026-0009

Strony:

113-127

Źródło:

AMCS, volume 36, number 1 (2026) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

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Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego