Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression

Abstract

Time series modeling and forecasting is an essential and hard task in financial engineering and optimization. Various models have been proposed in the literature and tested on daily data. However, a limited attention has been given to intraday data. In this regard, the current work presents a model for intraday stock price prediction that uses singular spectrum analysis (SSA) and support vector regression (SVR) coupled with particle swarm optimization (PSO). In particular, the SSA decomposes stock price time series into a small number of independent components used as predictors. The SVR is applied to the task of forecasting and PSO is employed to optimize SVR parameters. The performance of our proposed model is compared to the performance of four models widely used in financial prediction: the wavelet transform (WT) coupled with feedforward neural network (FFNN), autoregressive moving average (ARMA) process, polynomial regression (PolyReg), and naïve model. Finally, the mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean of squared errors (RMSE) are used as main performance metrics. By applying all models to six intraday stock price time series, the forecasting results from simulations show that the presented SSA-PSO-SVR largely outperforms the conventional WT-FFNN, ARMA, polynomial regression, and naïve model in terms of MAE, MAPE and RMSE. Therefore, our proposed predictive system SSA-PSO-SVR shows evident potential for noisy financial time series analysis and forecasting.