A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task - Zhang, Zichen; Ding, Shifei; Sun, Yuting (Neurocomputing (Amsterdam), 2020)
This work presents a hybrid model that combines support vector regression (SVR), empirical mode decomposition (EMD), the krill herd (KH) algorithm and a chaotic mapping function. EMD is used to decompose input time series data into components with several intrinsic mode functions (IMFs) and one residual, to capture the trends in the input data. SVR is used to forecast separately IMFs and the residual owing to its effectiveness in solving nonlinear regression and time series problems. The KH algorithm is used to select the parameters in the SVR models. The Tent chaotic mapping function is hybridized with the KH algorithm to prevent premature convergence and increase the accuracy of the whole model. Two real-world datasets from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are used to demonstrate the performance of the proposed EMD-SVRCKH model. The experimental results reveal that the proposed model provides competitive advantages over other models and offers greater forecasting accuracy.