An Improvised Method Using Neuro-Fuzzy System for Financial Time Series Forecasting

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