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

被引:5
|
作者
Vlasenko, Alexander [1 ]
Vlasenko, Nataliia [2 ]
Vynokurova, Olena [3 ,4 ]
Peleshko, Dmytro [3 ]
机构
[1] Kharkiv Natl Univ Radio Elect, Fac Comp Sci, Dept Artificial Intelligence, UA-61166 Kharkov, Ukraine
[2] Simon Kuznets Kharkiv Natl Univ Econ, Fac Econ Informat, Dept Informat & Comp Engn, UA-61166 Kharkov, Ukraine
[3] IT Step Univ, Dept Informat Technol, UA-79019 Lviv Oblast, Ukraine
[4] Kharkiv Natl Univ Radio Elect, Control Syst Res Lab, UA-61166 Kharkov, Ukraine
关键词
time series; neuro-fuzzy; membership function; backpropagation; Kachmarz method; Gaussian; prediction;
D O I
10.3390/data3040062
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页数:14
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