文章基本信息

标题：A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction
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作者：Alexander Vlasenko ; Nataliia Vlasenko , Olena Vynokurova ; Dmytro Peleshko 等
期刊名称：Data
印刷版ISSN：2306-5729
出版年度：2018
卷号：3
期号：4
页码：62-75
DOI：10.3390/data3040062
出版社：MDPI Publishing
摘要：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.
关键词：time series; neuro-fuzzy; membership function; backpropagation; Kachmarz method; Gaussian; prediction time series ; neuro-fuzzy ; membership function ; backpropagation ; Kachmarz method ; Gaussian ; prediction