Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.281138

Keywords:

stock price forecasting, correlation of technical indicators, neural network, adaptive neuro-fuzzy inference system, electric vehicle sector

Abstract

Adaptive neuro-fuzzy inference system (ANFIS) it is a type of neural network that combines the strengths of both fuzzy logic and artificial neural networks. ANFIS is particularly useful in stock trading because it can handle uncertainty and imprecision in the data, which is common in stock market data. In stock trading, ANFIS can be used for a variety of purposes, such as predicting stock prices, identifying profitable trades, and detecting stock market trends. One of the key advantages of using ANFIS for stock trading is that it can handle both linear and non-linear relationships in the data. This is particularly useful in the stock market, where the relationships between different variables are often complex and non-linear. ANFIS can also be updated and retrained as new data becomes available, which allows it to adapt to changing market conditions. Therefore, the main hypothesis of this work is to understand whether it is possible to predict the dynamics of prices for stocks of companies in the electric vehicle (EV) sector using technical analysis indicators. The purpose of this work is to create a model for predicting the prices of companies in the EV sector. The technical analysis indicators were processed by several machine learning models. Linear models generally perform worse than more advanced techniques. Decision trees, whether fine or coarse, tend to yield poorer performance results in terms of RMSE, MSE and MAE. After conducting a data analysis, the ANFIS and Bayesian regularization back propagation Neural Network (BR-BPNN) models were seen to be the most effective. The ANFIS was trained for 2000 epochs which yielded a minimum RMSE of 5.90926

Author Biographies

Alibek Barlybayev, L.N. Gumilyov Eurasian National University

Doctor PhD, Professor

Department of Artificial Intelligence Technologies

Lena Zhetkenbay, L.N. Gumilyov Eurasian National University

Doctor PhD, Tutor

Department of Artificial Intelligence Technologies

Didar Karimov, L.N. Gumilyov Eurasian National University

Student

Department of Artificial Intelligence Technologies

Banu Yergesh, L.N. Gumilyov Eurasian National University

PhD, Associated Professor

Department of Artificial Intelligence Technologies

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Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry

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Published

2023-08-31

How to Cite

Barlybayev, A., Zhetkenbay, L., Karimov, D., & Yergesh, B. (2023). Development neuro-fuzzy model to predict the stocks of companies in the electric vehicle industry. Eastern-European Journal of Enterprise Technologies, 4(4 (124), 72–87. https://doi.org/10.15587/1729-4061.2023.281138

Issue

Vol. 4 No. 4 (124) (2023): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2023 Alibek Barlybayev, Lena Zhetkenbay, Didar Karimov, Banu Yergesh

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