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 nonlinear 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
