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

1
Department of Artificial Intelligence, Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
2
Department of Informatics and Computer Engineering, Faculty of Economic Informatics, Simon Kuznets Kharkiv National University of Economics, 61166 Kharkiv, Ukraine
3
Information Technology Department, IT Step University, 79019 Lviv Oblast, Ukraine
4
Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
This paper is an extended version of the paper: A Hybrid Neuro-Fuzzy Model for Stock Market Time-Series Prediction. In Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, 21–25 August 2018; Lviv Polytechnic Publishing House: Lviv, Ukraine, 2018; pp. 352–356.
*
Author to whom correspondence should be addressed.
Received: 6 November 2018 / Revised: 6 December 2018 / Accepted: 6 December 2018 / Published: 8 December 2018
(This article belongs to the Special Issue Data Stream Mining and Processing)
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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. View Full-Text
Keywords: time series; neuro-fuzzy; membership function; backpropagation; Kachmarz method; Gaussian; prediction time series; neuro-fuzzy; membership function; backpropagation; Kachmarz method; Gaussian; prediction
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Vlasenko, A.; Vlasenko, N.; Vynokurova, O.; Peleshko, D. A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction. Data 2018, 3, 62.

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