Comparing training window selection methods for prediction in non-stationary time series.

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Bibliographic Details
Title: Comparing training window selection methods for prediction in non-stationary time series.
Authors: Petersen F; Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands., Haslbeck JMB; Psychological Methods Group, University of Amsterdam, Amsterdam, The Netherlands.; Department of Clinical Psychological Science, University of Maastricht, Maastricht, The Netherlands., Tendeiro JN; Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan., Langener AM; Department of Biomedical Data Science, Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA., Kas MJH; Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands., Rizopoulos D; Department of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands.; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands., Bringmann LF; Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands.
Source: The British journal of mathematical and statistical psychology [Br J Math Stat Psychol] 2026 May; Vol. 79 (2), pp. 341-361. Date of Electronic Publication: 2026 Jan 13.
Publication Type: Journal Article; Comparative Study; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 0004047 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2044-8317 (Electronic) Linking ISSN: 00071102 NLM ISO Abbreviation: Br J Math Stat Psychol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2044-8317
DOI:10.1111/bmsp.70018