Nonlinear Time Series Prediction Based on the Dynamic Characteristics Clustering Neural Network

2015 ◽  
Author(s):  
Lin Yi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Dynamic Characteristics ◽  
Time Series Prediction ◽  
Nonlinear Time Series

A Novel Modeling Method Based on Multi-Dimensional Taylor Network and its Application in Time Series Prediction

2014 ◽  
Vol 940 ◽  
pp. 480-484 ◽  
Author(s):  
Yi Lin ◽  
Hong Sen Yan ◽  
Bo Zhou
Keyword(s):  
Time Series ◽  
Prior Knowledge ◽  
Dynamic Characteristics ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Nonlinear Time Series ◽  
Modeling Method ◽  
Input Output ◽  
Output Data ◽  
Bridge Tower

A novel modeling method based on multi-dimensional Taylor network is proposed. The structure and the principle of the multi-dimensional Taylor network are introduced. Based on this, the method is applied in the nonlinear time series prediction based on multi-dimensional Taylor network. It provides a new method to predict the time series, which can describe the dynamic characteristics without prior knowledge and can realize the prediction of the nonlinear time series just with input-output data. An example of predicting the stress data of a large span bridge tower induced by strong typhoon is taken at last in this paper. Results indicate the validity and the better prediction accuracy of this method in nonlinear time series prediction.

A Prediction Method for Nonlinear Correlative Time Series

2010 ◽  
Vol 40-41 ◽  
pp. 930-936 ◽  
Author(s):  
Cong Gui Yuan ◽  
Xin Zheng Zhang ◽  
Shu Qiong Xu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Mutual Information ◽  
Orthogonal Polynomial ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Nonlinear Time Series ◽  
Iterative Learning ◽  
Polynomial Basis

A nonlinear correlative time series prediction method is presented in this paper.It is based on the mutual information of time series and orthogonal polynomial basis neural network. Inputs of network are selected by mutual information, and orthogonal polynomial basis is used as active function.The network is trained by an error iterative learning algorithm.This proposed method for nonlinear time series is tested using two well known time series prediction problems:Gas furnace data time series and Mackey-Glass time series.

Determining the input dimension of a neural network for nonlinear time series prediction

2003 ◽  
Vol 12 (6) ◽  
pp. 594-598 ◽  
Author(s):  
Zhang Sheng ◽  
Liu Hong-Xing ◽  
Gao Dun-Tang ◽  
Du Si-Dan
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Prediction ◽  
Nonlinear Time Series
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