A comparison of AdaBoost algorithms for time series forecast combination

Devon Barrow, S. Crone

    Research output: Contribution to journalArticlepeer-review

    66 Citations (Scopus)
    364 Downloads (Pure)

    Abstract

    Recently, combination algorithms from machine learning classification have been extended to time series regression, most notably seven variants of the popular AdaBoost algorithm. Despite their theoretical promise their empirical accuracy in forecasting has not yet been assessed, either against each other or against any established approaches of forecast combination, model selection, or statistical benchmark algorithms. Also, none of the algorithms have been assessed on a representative set of empirical data, using only few synthetic time series. We remedy this omission by conducting a rigorous empirical evaluation using a representative set of 111 industry time series and a valid and reliable experimental design. We develop a full-factorial design over derived Boosting meta-parameters, creating 42 novel Boosting variants, and create a further 47 novel Boosting variants using research insights from forecast combination. Experiments show that only few Boosting meta-parameters increase accuracy, while meta-parameters derived from forecast combination research outperform others.

    Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, [32, 4, (2016)] DOI: 10.1016/j.ijforecast.2016.01.006

    © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
    Original languageEnglish
    Pages (from-to)1103–1119
    JournalInternational Journal of Forecasting
    Volume32
    Issue number4
    Early online date1 Jun 2016
    DOIs
    Publication statusPublished - Oct 2016

    Bibliographical note

    NOTICE: this is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, [32, 4, (2016)] DOI: 10.1016/j.ijforecast.2016.01.006

    © 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

    Keywords

    • Forecasting
    • Time series
    • Boosting
    • Ensemble
    • Model combination
    • Neural networks

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