Some statistical and CI models to predict chaotic high-frequency financial data

Issue title: Special section: Decision Making Using Intelligent and Fuzzy Techniques

Guest editors: Cengiz Kahraman

Article type: Research Article

Authors: Marcek, Dusan^{; *}

Affiliations: Department of Applied Informatics, Faculty of Economics, VŠB – Technical University of Ostrava, Sokolská tř. 33, Ostrava, Czech Republic

Correspondence: [*] Corresponding author. Dusan Marcek, Department of Applied Informatics, Faculty of Economics, VŠB – Technical University of Ostrava, Sokolská tř. 33, Ostrava 70200, Czech Republic. E-mail: [email protected].

Abstract: To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Keywords: ARIMA models, neural networks, learning algorithms, time series forecasting

DOI: 10.3233/JIFS-189107

Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 6419-6430, 2020