Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163881
Title: Bayesian optimization based dynamic ensemble for time series forecasting
Authors: Du, Liang
Gao, Ruobin
Suganthan, Ponnuthurai Nagaratnam
Wang, David Zhi Wei
Keywords: Engineering::Civil engineering
Issue Date: 2022
Source: Du, 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. https://dx.doi.org/10.1016/j.ins.2022.01.010
Journal: Information Sciences
Abstract: Among 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.
URI: https://hdl.handle.net/10356/163881
ISSN: 0020-0255
DOI: 10.1016/j.ins.2022.01.010
Rights: © 2022 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles
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