Optimization of Local Prediction Algorithm of Financial Time Series Based on Fuzzy Neural Network

Abstract

A difficult area of study is financial time series forecasting. The development of time series and FNN are introduced in this paper, which also conducts a thorough study of local financial time series prediction. The next step is to propose and build a local forecasting model based on FNN for financial time series. The pseudo-inverse of the matrix is updated using ridge regression in this study in order to update the network parameters. This paper provides the corresponding incremental algorithm to update the network parameters as training input data or fuzzy rules increase, avoiding the need for parameter retraining. This paper employs MATLAB for simulation and comparative analysis in order to validate the viability and reliability of this approach. 96.31 percent is the predicted accuracy according to simulation results, which is 9.84 percent better than the predicted accuracy of the conventional NN algorithm. In terms of predicting financial time series, the model put forth in this paper performs better. The performance of the financial time series prediction model is further enhanced, making up for the shortcomings of the earlier research. Additionally, it contributes to related research in the area of financial time series prediction.