A forecasting model of time series based on wavelet neural network

2012 ◽  
Author(s):  
Yong Peng ◽  
Zhineng Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wavelet Neural Network ◽  
Forecasting Model

scholarly journals A Wavelet Neural Network Forecasting Model Based on ARIMA

2013 ◽  
Vol 347-350 ◽  
pp. 3013-3018 ◽  
Author(s):  
Bin Wang ◽  
Wen Ning Hao ◽  
Gang Chen ◽  
Deng Chao He ◽  
Bo Feng
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Arima Model ◽  
Wavelet Neural Network ◽  
Stock Index ◽  
Forecasting Model ◽  
Combined Model ◽  
Bp Network ◽  
Model Based ◽  
Time Series Mining

Stock index series is Non-stationary, Nonlinear and factors with impact on stock index fluctuation are complex, a time series forecasting model combined ARIMA model and wavelet neural network is presented. The combined model uses BP neural network as the main framework, uses wavelet basis function instead of transfer function in the network, also add some inner factors of the time series mining by ARIMA model, as the part impute of Wavelet Neural Network. So it is more scientific and rational that using inner factors and external other factors. The last simulate experiment shows that the wavelet neural network forecasting model based on ARIMA has higher accuracy than ARIMA model or BP network.

Wavelet neural network model for river flow time series

2012 ◽  
Vol 165 (8) ◽  
pp. 425-439 ◽  
Author(s):  
Budu Krishna ◽  
Yellamelli Ramji Satyaji Rao ◽  
Purna Chandra Nayak
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
River Flow ◽  
Flow Time ◽  
Wavelet Neural Network

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.

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

Recurrent Wavelet Neural Network Based on Immune Evolving Algorithm and its Application in Nonlinear Modeling

2014 ◽  
Vol 1049-1050 ◽  
pp. 1666-1669
Author(s):  
Xiang Jie Luo ◽  
Cheng Kai Wei ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Nonlinear Modeling ◽  
Wavelet Neural Network ◽  
Chaotic Time Series ◽  
Good Prediction ◽  
Training Algorithm ◽  
Connection Weight ◽  
Scaling Parameter ◽  
Prediction Capability

To improve the modeling performance of Recurrent Wavelet Neural Network (RWNN), a training algorithm based on Immune Evolving Algorithm (IEA) is proposed. In the process of RWNN training, IEA is mainly used to optimize the connection weight, translating and scaling parameter. The experiment result on Duffing chaotic time series shows that the proposed RWNN training algorithm has a good prediction capability in the field of nonlinear modeling.

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