Intelligent optimal recursive neural network method of time series prediction

Abstract

The invention provides an intelligent optimal recursive neural network method of time series prediction, and relates to time series prediction analysis. A time series prediction model based on recurrent predictor neural network (RPNN) combined with a simulated annealing particle swarm optimization (SAPSO) algorithm takes chaos and phase-space reconstruction as theoretical basis so as to realize nonlinear time series prediction. The method comprises the steps of calculating time series attractor dimensionality by a saturated correlation dimension method, and carrying out phase-space reconstruction by selecting embedded dimension and delaying time. The structure of the RPNN is only decided by the embedded dimension, and the SAPSO hybrid optimization algorithm is adopted by network training. The SAPSO algorithm is combined with the rapid convergence characteristic of a PSO algorithm and the complete searching characteristic of an SA algorithm; the method is capable of expanding the optimal searching range while maintaining the rate of convergence, so that the aim of not falling into local extremum is realized; the intelligent optimal recursive neural network method is mainly used for the nonlinear time series prediction.

Description

technical field [0001] The invention relates to time series prediction analysis, in particular to an intelligent optimization recursive neural network method for time series prediction. Background technique [0002] The analysis and prediction of time series has important application value in many fields. However, most of the linear models used in the early time series forecast analysis have certain limitations in theory and method. Most systems have complex nonlinear characteristics. The introduction of nonlinear research paradigms to analyze and predict time series, and approximate description of chaotic dynamical systems through nonlinear iteration and learning models is the inevitable result of the development of nonlinear time series forecasting theory. [0003] Chaos is one of the nonlinear characteristics of time series. Chaos prediction theory holds that: on the one hand, the deterministic characteristics of chaos make many seemingly random appearances actually pre...

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 fundamentally 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

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