Gandhi, Mohd. Asif  <br/><br/>
Improvised method using neuro-fuzzy system for financial time series forecasting 

 - Vol.14(4), Apr 
 - Chennai  ICT Academy  2024 
 - 3328-3333p. 
<br/><br/> Financial time series forecasting is crucial for making informed<br/>investment decisions. This study proposes an improvised method<br/>utilizing a Neuro-Fuzzy System (NFS) for enhanced forecasting<br/>accuracy. Traditional forecasting methods often struggle with the<br/>nonlinear and dynamic nature of financial time series data. NFS<br/>integrates neural network and fuzzy logic techniques, offering a robust<br/>framework for modeling complex relationships within financial data.<br/>The proposed method employs NFS to adaptively learn and model the<br/>intricate patterns present in financial time series data. It combines the<br/>strengths of neural networks in learning complex patterns and fuzzy<br/>logic in handling uncertainty and imprecision. This study contributes<br/>by introducing an innovative approach to financial time series<br/>forecasting, leveraging the capabilities of NFS to improve forecasting<br/>accuracy and reliability. Experimental results demonstrate the<br/>effectiveness of the proposed method in accurately forecasting<br/>financial time series data. The method outperforms traditional<br/>forecasting techniques, showcasing its potential for practical<br/>applications in financial markets. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Computer Engineering