A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China

Due to the distinct seasonal characteristics of hydropower, this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for Chinese hydropower consumption forecasting. In the formulation of ensemble learning model, the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular component. Then the LSSVR with the radial basis function (RBF) kernel is used to predict the three different components independently. Finally, these prediction results of the three components are combined with another LSSVR to formulate an ensemble result for the original hydropower consumption series. In terms of error measurements and statistic test on the forecasting performance, the proposed approach outperforms all the other benchmark methods listed in this study in both level accuracy and directional accuracy. Experimental results reveal that the proposed SD-based LSSVR ensemble learning paradigm is a very promising approach for complex time series forecasting with seasonality.

► A novel seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning paradigm is proposed.
► The proposed SD-based LSSVR ensemble learning approach is suitable for time series prediction with seasonality.
► The proposed SD-based LSSVR ensemble learning approach is very useful for both one-step ahead forecasting and multi-step ahead forecasting.