Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China
We observe oscillated, nonlinear and non-stationary features of HSR flows. We propose a hybrid approach combining EEMD and GSVM for short-term prediction of HSR flows. We use mode reconstruction instead of a simple summation of each IMF. We apply the approach to a real-world HSR in China.
high-speed rail (hsr) demand forecasting hybrid model ensemble empirical mode decomposition (eemd) grey support vector machine (gsvm)