Short-term prediction of BP neural network based on difference method

被引：2
|
作者：
Hu Menghui [1 ]
Liu Yian [1 ]
机构：
[1] Jiangnan Univ, Sch Artificil Intelligence & Comp Sci, Wuxi, Jiangsu, Peoples R China
来源：
2020 19TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS FOR BUSINESS ENGINEERING AND SCIENCE (DCABES 2020) | 2020年
关键词：
differencemethods; BP neural net; time series prediction;
D O I：
10.1109/DCABES50732.2020.00024
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Aiming at the problem of short-term time series forecasting, a neural network based on the difference method(DMBP) is proposed.And then using sunspot data and Mackey-Glass chaotic time series data to test the performance of DMBP.In the experiment,DMBP,BP neural network algorithms, support vector regression machine (SVR),and autoregressive integrated moving average model (ARIMA) are compared in two cases with a prediction length of 2, 5. Experiment resaults show that the prediction accuracy of the DMBP algorithm is significantly improved compared to the BP neural network, and it is far better than SVR. It is equivalent to the ARIMA algorithm in short-term prediction, but the DMBP modeling process is simpler than ARIMA.
引用
收藏
页码：58 / 61
页数：4
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