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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| Vydané v: | Expert systems with applications Ročník 166; s. 114091 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Elsevier Ltd
15.03.2021
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •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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| AbstractList | 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. •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. |
| ArticleNumber | 114091 |
| Author | Sun, Xiaolei Li, Jianping Feng, QianQian Liu, Mingxi Hao, Jun |
| Author_xml | – sequence: 1 givenname: Jianping surname: Li fullname: Li, Jianping email: ljp@casisd.cn organization: Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China – sequence: 2 givenname: Jun surname: Hao fullname: Hao, Jun email: haojun181@mails.ucas.ac.cn organization: Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China – sequence: 3 givenname: QianQian surname: Feng fullname: Feng, QianQian email: fengqianqian18@mails.ucas.ac.cn organization: Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China – sequence: 4 givenname: Xiaolei orcidid: 0000-0001-5640-5240 surname: Sun fullname: Sun, Xiaolei email: xlsun@casisd.cn organization: Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China – sequence: 5 givenname: Mingxi surname: Liu fullname: Liu, Mingxi email: liumingxi@casipm.ac.cn organization: Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China |
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| Keywords | Multi-objective optimization Ensemble forecasting Baltic Dry Index Evolutionary algorithm Machine learning |
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| Snippet | •A heterogeneous ensemble forecasting model of nonlinear time series is proposed.•Both forecasting error and model divergence are considered.•Dynamic... The excellent generalization performance of time series ensemble forecasting depends on the accuracy and diversity of the individual models. In this paper, a... |
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| SubjectTerms | 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 |
| Title | Optimal selection of heterogeneous ensemble strategies of time series forecasting with multi-objective programming |
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