Coupling Firefly Algorithm and Least Squares Support Vector Regression for Crude Oil Price Forecasting

Abstract

To improve the prediction accuracy of crude oil price even in current complicated international situation, this paper proposed a novel model linking firefly algorithm (FA) with least squares support vector regression (LSSVR), namely FA-LSSVR. In this hybrid intelligent model, FA is used to find the optimal values of LSSVR parameters (i.e., penalty coefficient and kernel function parameters), in order to achieve fast and accurate prediction results. To evaluate the forecasting ability of FA-LSSVR, its performance is compared with other models, including hybrid intelligent methods (LSSVR models with other popular optimization methods), and single models with given predetermined parameters (i.e., support sector regression (SVR), LSSVR, back-propagation neural network (BPNN), autoregressive integrated moving average (ARIMA)). The empirical results reveal that FA-LSSVR outperforms other benchmarks in terms of prediction accuracy, time saving and robustness, suggesting that the proposed approach is a promising alternative to forecast the crude oil price.