An improved support vector model in car-rental revenue forecast

Abstract

Monthly revenue per unit (RPU), measured by the total monthly revenue divided by the total monthly number of cars in the fleet, is one of the most popular profitability assessments in the car-rental industry. Because pricing decision is influenced by profit a lot, accurate forecast of RPU is thus useful for pricing in car-rental industry. Support vector regression (SVR) is an emerging forecast technique and successfully applied in many domains. The aim of this study is to investigate the feasibility of SVR in RPU forecast. Because SVR model contains three parameters influencing the forecasting accuracy, this work treats the forecasting error as an objective and employs tabu search (TS) algorithms to minimize the forecasting error. Actual RPU data are used to depict the forecasting performance of the improved SVR model. Two forecasting model, namely the general regression neural network (GRNN) model and the autoregressive integrated moving average (ARIMA) model, are used to forecast the same RPU data. Simulation results indicate that the improved SVR model is superior to the other models in terms of forecasting accuracy.