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

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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
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  Data: Comparing training window selection methods for prediction in non-stationary time series.
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  Data: <searchLink fieldCode="AU" term="%22Petersen+F%22">Petersen F</searchLink>; Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands.<br /><searchLink fieldCode="AU" term="%22Haslbeck+JMB%22">Haslbeck JMB</searchLink>; Psychological Methods Group, University of Amsterdam, Amsterdam, The Netherlands.; Department of Clinical Psychological Science, University of Maastricht, Maastricht, The Netherlands.<br /><searchLink fieldCode="AU" term="%22Tendeiro+JN%22">Tendeiro JN</searchLink>; Graduate School of Advanced Science and Engineering, Hiroshima University, Higashihiroshima, Japan.<br /><searchLink fieldCode="AU" term="%22Langener+AM%22">Langener AM</searchLink>; Department of Biomedical Data Science, Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.<br /><searchLink fieldCode="AU" term="%22Kas+MJH%22">Kas MJH</searchLink>; Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, The Netherlands.<br /><searchLink fieldCode="AU" term="%22Rizopoulos+D%22">Rizopoulos D</searchLink>; Department of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands.; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.<br /><searchLink fieldCode="AU" term="%22Bringmann+LF%22">Bringmann LF</searchLink>; Department of Psychometrics and Statistics, Faculty of Behavioural and Social Sciences, University of Groningen, Groningen, The Netherlands.
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  Data: <searchLink fieldCode="JN" term="%220004047%22">The British journal of mathematical and statistical psychology</searchLink> [Br J Math Stat Psychol] 2026 May; Vol. 79 (2), pp. 341-361. <i>Date of Electronic Publication: </i>2026 Jan 13.
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        Value: 10.1111/bmsp.70018
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        Text: English
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        StartPage: 341
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      – TitleFull: Comparing training window selection methods for prediction in non-stationary time series.
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              Text: 2026 May
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