Quantum-behaved particle swarm optimization (QPSO) recurrent predictor neural network (RPNN) method for financial time series prediction

Abstract

The invention discloses a quantum-behaved particle swarm optimization (QPSO) recurrent predictor neural network (RPNN) method for financial time series prediction, which relates to analysis and prediction on a time sequence. Chaos and phase space reconstruction theories are firstly applied, through a saturation correlation dimension (G-P) method, a chaotic financial time series attractor dimension is calculated, and the structure of the RPNN is determined; then, the RPNN is trained by the QPSO algorithm; and finally, the dynamic optimal weight and the threshold of the network are determined, and thus, the simulation prediction value of the RPNN and the actual value can reach the minimum error precision. The problem that optimization of the RPNN based on a gradient algorithm is easy to fall into local minimum can be solved, and the built QPSO-RPNN optimization prediction method can be widely applied in financial investment and social economy, and has the advantages that the convergence rate is quick, the searching is global, the programming is simple and efficient, and the prediction precision is high.

Description

technical field [0001] The invention relates to the analysis and prediction of time series, in particular to a quantum particle swarm optimization recursive neural network method for financial time series prediction. Background technique [0002] The analysis and prediction of time series has important application value in many fields. Most of the early analysis methods for time series forecasting are linear models, which have certain limitations in theory and method. Most systems have complex nonlinear characteristics. It is an inevitable trend in the development of nonlinear time series forecasting theory to introduce nonlinear research paradigms to analyze and predict time series, and to approximate chaotic dynamical systems through nonlinear iteration and learning models. [1] . [0003] The financial market is a complex nonlinear dynamic system due to the influence of many factors. The non-stationarity and weak chaos of the financial time series are the comprehensive e...

AI Technical Summary

Problems solved by technology

Chaos prediction theory holds that: on the one hand, the deterministic characteristics of chaos make many seemingly random appearances actually predictable; on the other hand, the inherent extreme sensitivity of chaotic phenomena to initial conditions is fundamental. limits its long-term predictive effect

Therefore, the short-term evolution trend of the chaotic dynamical system is predictable, but the long-term prediction is not realistic [4]

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