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

被引：12
|
作者：
Vlasenko, Alexander [1 ]
Vlasenko, Nataliia [2 ]
Vynokurova, Olena [3 ,4 ]
Bodyanskiy, Yevgeniy [1 ,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, Informat Technol Dept, UA-79019 Lvov, Ukraine
[4] Kharkiv Natl Univ Radio Elect, Control Syst Res Lab, UA-61166 Kharkov, Ukraine
来源：
DATA | 2019年 / 4卷 / 03期
关键词：
time series; neuro-fuzzy; ensemble; model averaging; Gaussian; prediction; stochastic gradient descent; STOCK-MARKET; OPTIMIZATION; INTEGRATION; NETWORKS; SYSTEMS;
D O I：
10.3390/data4030126
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页数：11
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