Time Series Forecasting with an EMD-LSSVM-PSO Ensemble Adaptive Learning Paradigm
| Autor: | Chengjie Zhou, Tiejun Jiang, Huaiqiang Zhang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Series (mathematics) Computer science Particle swarm optimization 02 engineering and technology Residual 01 natural sciences Least squares Support vector machine Least squares support vector machine 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Adaptive learning Time series Algorithm 0105 earth and related environmental sciences |
| Zdroj: | Proceedings of the 2018 International Conference on Computational Intelligence and Intelligent Systems. |
| DOI: | 10.1145/3293475.3293477 |
| Popis: | In this study, an empirical mode decomposition (EMD) based least square support vector machine (LSSVM) ensemble adaptive learning paradigm is proposed for time series forecasting. For this purpose, the original time series are first decomposed into several intrinsic mode functions (IMFs) and one residual component. Then phase space reconstruction (PSR) is done in each component, where the samples are put into LSSVM for training. Particle swarm optimization algorithm (PSO) is used to achieve the adaptive optimization of forecasting models in different components, which makes LSSVMs better describe the signal characteristics in different scales, thus greatly improves the efficiency and accuracy of the learning and training; Finally, the forecasting values of the original series are obtained through the reconstruction of the forecasting values in each component. For illustration and verification, the spare parts cost series for a vessel is used to test the effectiveness of the proposed EMD-LSSVM-PSO ensemble adaptive learning methodology. Empirical results obtained demonstrate the attractiveness of the proposed method. |
| Databáze: | OpenAIRE |
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