Time series prediction based on EMD-LSTM model
Shaowu Dai, Qiangqiang Chen, Zhihao Liu, Hongde Dai · JOURNAL OF SHENZHEN UNIVERSITY SCIENCE AND ENGINEERING · 2020
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.