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dc.contributor.authorDu, Liangen_US
dc.contributor.authorGao, Ruobinen_US
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnamen_US
dc.contributor.authorWang, David Zhi Weien_US
dc.identifier.citationDu, L., Gao, R., Suganthan, P. N. & Wang, D. Z. W. (2022). Bayesian optimization based dynamic ensemble for time series forecasting. Information Sciences, 591, 155-175.
dc.description.abstractAmong various time series (TS) forecasting methods, ensemble forecast is extensively acknowledged as a promising ensemble approach achieving great success in research and industry. Due to the high diversification of individual model assumptions, heterogeneous information fusion contributes to generating effective and robust forecasts for Economics, Meteorology, and Transportation. This paper proposes a Bayesian optimization-based dynamic ensemble (BODE) that overcomes the single model-based methods limitation and provides a dynamic ensemble forecast combination for TS with time-varying underlying patterns. The proposed BODE method combines ten disparate model candidates, including statistical methods, machine learning (ML)-based models, and the latest deep neural networks (DNN). We take into consideration their prediction performance for the recent past to adjust their weights for combination and apply the model-based Bayesian optimization algorithm (BOA) for the combination hyperparameter (HP) tuning to endow our method with higher adaptability and better generalization performance. Besides, the frequency impact of TS data on the ensemble forecast methods is under-researched in the current literature. Therefore, four groups of distinct seasonal TS datasets are investigated in this paper. The empirical result demonstrates that our method performs robustly better performance with the main reasons analyzed in a detailed ablation study.en_US
dc.relation.ispartofInformation Sciencesen_US
dc.rights© 2022 Elsevier Inc. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleBayesian optimization based dynamic ensemble for time series forecastingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.subject.keywordsTime Series Forecastingen_US
dc.subject.keywordsForecast Combinationen_US
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