A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction

Alexander Vlasenko, Nataliia Vlasenko, Olena Vynokurova 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 Oblast, Ukraine
Dmytro Peleshko: Information Technology Department, IT Step University, 79019 Lviv Oblast, Ukraine

Data, 2018, vol. 3, issue 4, 1-14

Abstract: Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This paper proposes a novel multivariate hybrid neuro-fuzzy model for forecasting tasks, which is based on and generalizes the neuro-fuzzy model with consequent layer multi-variable Gaussian units and its learning algorithm. The model is distinguished by a separate consequent block for each output, which is tuned with respect to the its output error only, but benefits from extracting additional information by processing the whole input vector including lag values of other variables. Numerical experiments show better accuracy and computational performance results than competing models and separate neuro-fuzzy models for each output, and thus an ability to implicitly handle complex cross correlation dependencies between variables.

Keywords: time series; neuro-fuzzy; membership function; backpropagation; Kachmarz method; Gaussian; prediction (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
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