Time series predicting model

Abstract

The invention belongs to the field of the analysis of nonlinear time series, in particular to a time series predicting model. The network model comprises an input layer, a middle layer and an output layer, wherein the middle layer unit consists of a chaos operator with the chaos characteristic. The parameter of the chaos operator is trained and adjusted through a chaos optimization algorithm. By learning and training, the network predicts the value at a certain time in future with the previous known value of the time series, and gradually modifies the parameter of the chaos operator according a predicted error, thereby gradually having the information which is accordant with the regularity in the time series and completing the predicting function of the time series. Particularly, the model can effectively realize the multi-step prediction of the time series. The time series is mainly used for the field of the prediction and the analysis of the time series in the practical engineering.

Description

technical field [0001] The invention belongs to the field of nonlinear time series analysis, and relates to a class of network models applied to actual engineering time series prediction. The invention provides a new predictive network model—a predictive network model based on chaotic operators. Background technique [0002] Time series predictive analysis technology has important application value in many fields such as economy, meteorology, geology, hydrology, military affairs, and medicine. Scientifically and correctly predicting and analyzing various actual time series can produce huge economic and social benefits. Due to the complex nonlinear characteristics of the actual system, the linear and nonlinear models used in the early time series analysis have certain limitations in theoretical analysis and practical application. In recent years, many artificial intelligence methods are often used in the uncertainty analysis of time series. In particular, the research of c...

AI Technical Summary

Problems solved by technology

[0004] The main problem of this kind of prediction method is that the parameters of phase space reconstruction are not easy to select, and at the same time, the neural network has problems such as slow training speed, insufficient dynamic characteristics and easy to fall into local minimum in the process of predicting the actual engineering time series. This type of forecasting method usually has good forecasting results for theoretical data, but the forecasting effect for actual engineering sequences is not ideal.

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