Application of hybrid genetic algorithm and simulated annealing in a SVR traffic flow forecasting model

Abstract

Due to complex nonlinear data pattern in time series regression, forecasting techniques had been categorized in different ways, and the literature is also full of differing opinions, thus, it is difficult to make a general conclusion. In the recent years, the support vector regression (SVR) model has been widely used to solve nonlinear time series regression problems. This investigation presents a short-term traffic forecasting model by employing SVR with genetic algorithm and simulated annealing algorithm (GA-SA) to determine the suitable parameter combination in the SVR model. Consequently, a numerical example of traffic flow values from northern Taiwan is used to demonstrate the forecasting performance of the proposed SVRGA-SA model is superior to the seasonal autoregressive integrated moving average (SARIMA) time series model. Index Terms-Support vector regression; genetic algorithm with simulated annealing (GA-SA); hybrid algorithms; SARIMA; traffic flow forecasting.