DAI Shaowu,CHEN Qiangqiang,LIU Zhihao,et al.Time series prediction based on EMD-LSTM model[J].Journal of Shenzhen University Science and Engineering,2020,37(3):265-270.[doi:10.3724/SP.J.1249.2020.03265]





Time series prediction based on EMD-LSTM model
1)海军航空大学岸防兵学院,山东烟台 264000
2) 海军92728部队,上海 200040
3)海军92214部队,浙江宁波 315000
4)海军航空大学航空基础学院,山东烟台 264000
DAI Shaowu1 CHEN Qiangqiang1 2 LIU Zhihao3 and DAI Hongde4
1) College of Coastal Defense, Naval Aviation University, Yantai 264000, Shandong Province, P.R.China
2) Naval 92728, Shanghai 200040, P.R.China
3) Naval 92214, Ningbo 315000, Zhejiang Province, P.R.China
4) College of Basic Sciences for Aviation, Naval Aviation University, Yantai 264000, Shandong Province, P.R.China
数理统计学 时间序列预测 经验模态分解 长短期记忆网络 PM2.5 机器学习 时间序列分解
mathematical statistics time series prediction empirical mode decomposition long-short term memory network PM2.5 machine learning time series decompose
The time series in engineering applications are mostly non-stationary and non-linear, which are difficult to be directly predicted. Based on the empirical model decomposition (EMD) method, we decompose the original time series into a number of intrinsic mode functions (IMFs) and trend series with the different feature scales in order to reduce the complexity of time series. Meanwhile, in the prediction process, in order to solve the problems of training difficulty and the gradient disappearance in recurrent neural network (RNN) model, we introduce a long-short term memory (LSTM) network algorithm to predict both the results of the decomposed IMF components and trend series respectively, and obtain the final prediction result by surposing the respective prediction results. Taking the PM2.5 concentration in Beijing as an example for prediction and analysis, we compare our prediction algorithm with the single prediction algorithm. The results show that the proposed prediction model has higher accuracy and can meet the prediction requirements.


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Foundation:Natural Science Foundation of Shandong Province (ZR2017MF036); National Defense Science and Technology Foundation of China(F062102009)
Corresponding author:Engineer CHEN Qiangqiang. E-mail: 1195275597@qq.com
Citation:DAI Shaowu, CHEN Qiangqiang, LIU Zhihao, et al. Time series prediction based on EMD-LSTM model[J]. Journal of Shenzhen University Science and Engineering, 2020, 37(3): 265-270.(in Chinese)
基金项目:山东省自然科学基金资助项目(ZR2017MF036); 国防科技基金资助项目(F062102009)
引文:戴邵武,陈强强,刘志豪,等.基于EMD-LSTM的时间序列预测方法[J]. 深圳大学学报理工版,2020,37(3):265-270.
更新日期/Last Update: 2020-05-30