Research Article
Research on a Hybrid EMD-SVR Model for Time Series Prediction
@INPROCEEDINGS{10.1007/978-3-030-66922-5_9, author={Qiangqiang Yang and Dandan Liu and Yong Fang and Dandan Yang and Yi Zhou and Ziheng Sheng}, title={Research on a Hybrid EMD-SVR Model for Time Series Prediction}, proceedings={Security and Privacy in New Computing Environments. Third EAI International Conference, SPNCE 2020, Lyngby, Denmark, August 6-7, 2020, Proceedings}, proceedings_a={SPNCE}, year={2021}, month={1}, keywords={Time series Empirical Mode Decomposition Support Vector Regression Building energy consumption Prediction}, doi={10.1007/978-3-030-66922-5_9} }- Qiangqiang Yang
Dandan Liu
Yong Fang
Dandan Yang
Yi Zhou
Ziheng Sheng
Year: 2021
Research on a Hybrid EMD-SVR Model for Time Series Prediction
SPNCE
Springer
DOI: 10.1007/978-3-030-66922-5_9
Abstract
Time series prediction methods were widely used in various fields. The prediction method for non-stationary and nonlinear time series was studied in this paper. This method decomposed non-stationary time series into stationary sub-sequences using the Empirical Mode Decomposition method. And then an appropriate time-step was chosen and the Support Vector Regression algorithm was applied to predict each stationary sub-sequence. The sum of predicted values was the forecasting results of the original sequence. The method was applied to building energy consumption datasets, which were collected in some buildings. The experimental results showed that the hybrid algorithm of Support Vector Regression and Empirical Mode Decomposition had higher accuracy and was suitable for predicting non-linear and non-stationary time series. Moreover, this hybrid algorithm was used to predict the time series with outliers and to test its noise-resistant performance. The forecasting results also illustrated EMD-SVR algorithm was more robust than SVR algorithm.
