Improvised method using neuro-fuzzy system for financial time series forecasting
Gandhi, Mohd. Asif
Improvised method using neuro-fuzzy system for financial time series forecasting - Vol.14(4), Apr - Chennai ICT Academy 2024 - 3328-3333p.
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.
Computer Engineering
Improvised method using neuro-fuzzy system for financial time series forecasting - Vol.14(4), Apr - Chennai ICT Academy 2024 - 3328-3333p.
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.
Computer Engineering