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

Salim Lahmiri

Applied Mathematics and Computation, 2018, vol. 320, issue C, 444-451

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.

Keywords: Intraday stock price; Time series; Singular spectrum analysis; Support vector regression; Particle swarm optimization; Forecasting (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300317306859
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:320:y:2018:i:c:p:444-451

DOI: 10.1016/j.amc.2017.09.049

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().