A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Alexander Vlasenko, Nataliia Vlasenko, Olena Vynokurova, Yevgeniy Bodyanskiy and Dmytro Peleshko
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Alexander Vlasenko: Department of Artificial Intelligence, Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
Nataliia Vlasenko: Department of Informatics and Computer Engineering, Faculty of Economic Informatics, Simon Kuznets Kharkiv National University of Economics, 61166 Kharkiv, Ukraine
Olena Vynokurova: Information Technology Department, IT Step University, 79019 Lviv, Ukraine
Yevgeniy Bodyanskiy: Department of Artificial Intelligence, Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
Dmytro Peleshko: Information Technology Department, IT Step University, 79019 Lviv, Ukraine

Data, 2019, vol. 4, issue 3, 1-11

Abstract: Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

Keywords: time series; neuro-fuzzy; ensemble; model averaging; Gaussian; prediction; stochastic gradient descent (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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