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

Autor:	Dusan Marcek
Rok vydání:	2020
Předmět:	Statistics and Probability 0209 industrial biotechnology 020901 industrial engineering & automation Artificial Intelligence Computer science 0202 electrical engineering electronic engineering information engineering General Engineering Chaotic Econometrics 020201 artificial intelligence & image processing 02 engineering and technology
Zdroj:	Journal of Intelligent & Fuzzy Systems. 39:6419-6430
ISSN:	1875-8967 1064-1246
DOI:	10.3233/jifs-189107
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a63ce0058df750dfcf472e6b008a36b9 https://doi.org/10.3233/jifs-189107 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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