Optimal selection of heterogeneous ensemble strategies of time series forecasting with multi-objective programming

•A heterogeneous ensemble forecasting model of nonlinear time series is proposed.•Both forecasting error and model divergence are considered.•Dynamic heterogeneous mutation operator is introduced to improve MOPSO.•The proposed model has excellent prediction performance and robustness. The excellent...

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Bibliographic Details
Published in:Expert systems with applications Vol. 166; p. 114091
Main Authors: Li, Jianping, Hao, Jun, Feng, QianQian, Sun, Xiaolei, Liu, Mingxi
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 15.03.2021
Elsevier BV
Subjects:
Algorithms
Baltic Dry Index
Ensemble forecasting
Evolutionary algorithm
Forecasting
Lead time
Machine learning
Mathematical programming
Model accuracy
Multi-objective optimization
Multiple objective analysis
Mutation
Particle swarm optimization
Statistical models
Statistical tests
Time series
Multi-objective optimization
Ensemble forecasting
Baltic Dry Index
Evolutionary algorithm
Machine learning
ISSN:0957-4174, 1873-6793
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Summary:•A heterogeneous ensemble forecasting model of nonlinear time series is proposed.•Both forecasting error and model divergence are considered.•Dynamic heterogeneous mutation operator is introduced to improve MOPSO.•The proposed model has excellent prediction performance and robustness. The excellent generalization performance of time series ensemble forecasting depends on the accuracy and diversity of the individual models. In this paper, a heterogeneous ensemble forecasting model with multi-objective programming for nonlinear time series is proposed. Accordingly, an improved multi-objective particle swarm optimization (MOPSO) algorithm integrated with a dynamic heterogeneous mutation operator is designed. The nonlinear time series of the Baltic Dry Index (BDI) is selected as the forecasting object to train, validate and test the ensemble forecasting model established in this paper. To verify the superior forecasting performance of the proposed model, 20 forecasting models including statistical models, machine learning models, and optimization algorithm–based ensemble models are utilized and compared. The experimental results under different lead times revealed that: 1) the forecasting approach with multi-objective programming has excellent robustness and can effectively exert out-of-sample prediction under different lead times for nonlinear time series; 2) with the increase of lead time, the out-of-sample forecasting performance would gradually decrease for all models, and the precision of the ensemble forecasting model is better than that of the individual forecasting model; 3) the forecasting performance of the MOPSO with crowding distance (MOPSOCD)-based ensemble forecasting model is better than that of benchmark machine learning models and other optimal ensemble forecasting models in terms of the prediction accuracy and statistical test results.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2020.114091