Local Linear Wavelet Neural Network with Weight Perturbation Technique for Time Series Prediction

2008 ◽  
Author(s):  
Zongmei Zhang ◽  
Zhifang Sun ◽  
Hiroki Tamura
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Perturbation Technique ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Local Linear

Time-series prediction using a local linear wavelet neural network

2006 ◽  
Vol 69 (4-6) ◽  
pp. 449-465 ◽  
Author(s):  
Yuehui Chen ◽  
Bo Yang ◽  
Jiwen Dong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Local Linear

Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 2636-2640 ◽  
Author(s):  
Jian Hua Zhang ◽  
Fan Tao Kong ◽  
Jian Zhai Wu ◽  
Meng Shuai Zhu ◽  
Ke Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Agricultural Production ◽  
Supply And Demand ◽  
Time Series Prediction ◽  
Wavelet Neural Network ◽  
Agricultural Products ◽  
Price Model ◽  
Agricultural Prices

Accurate prediction of agricultural prices is beneficial to correctly guide the circulation of agricultural products and agricultural production and realize the equilibrium of supply and demand of agricultural area. On the basis of wavelet neural network, this paper, choosing tomato prices as study object, tomato retail price data from ten collection sites in Hebei province from January, 1st, 2013 to December, 30th, 2013 as samples, builds the tomato price time series prediction model to test price model. As the results show, model prediction error rate is less than 0.01, and the correlation (R2) of predicted value and actual value is 0.908, showing that the model could accurately predict tomatoes price movements. The establishment of the model will provide technical support for tomato market monitoring and early warning and references for related policies.

Research on the Magnitude Time Series Prediction Based on Wavelet Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 233-236 ◽  
Author(s):  
Yan Lan Chen ◽  
Yi Chen ◽  
Qing Huang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Predictive Performance ◽  
Wavelet Neural Network ◽  
Single Step ◽  
Reconstructed Phase Space ◽  
Takens Theorem

Based on the fundamental principles of the wavelet analysis combining with BP neural network, the paper can obtain the minimum embedding dimension and delay time. According to the chaos theory, the phase space of the magnitude time series can be reconstructed by Takens theorem. The paper uses wavelet neural network to train and test the nonlinear magnitude time series in the reconstructed phase space. The simulation results show that the predictive effect of the magnitude time series is remarkable and the predictive performance of single-step prediction is superior to that of multi-step prediction.

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