A New Forecasting Algorithm Parameters Solving Method

Abstract

Abstract.To improve the forecasting accuracy of oil prices, this paper has proposed oil price predicting model (PSR-LSSVM) based on unified solving by phase space reconstruction and predicting algorithm parameters using interrelation between phase-space reconstruction and predicting algorithm. The LSSVM is selected as the predicting algorithm of oil prices, and the parameters of phase space reconstruction and LSSVM are taken as individuals of the genetic algorithm, and the optimal delay time, embedding dimension and LSSVM parameters are obtained through selection, crossover and mutation evolutionary mechanism, and finally, the predicting model of oil prices is established and the performance of predicting model is tested by Daqing oil price time series. The results show that the proposed model PSR-LSSVM obtains higher predicting accuracy than the oil-price forecasting models of independently optimized phase-space reconstruction and LSSVM, which provides a new research idea for the predicting problem of chaotic time series.