Time series prediction method based on Convolutional Autoencoder and LSTM

Many time series data are characterized by strong randomness and high noise.The traditional predictive model is difficult to extract the characteristics of the data, and the prediction effect is not very good. Convolutional neural networks and autoencoder have a good effect on extracting data features. Combining these two techniques, a predictive model of a combination of convolutional autoencoder(CAE) and Long Short Term Memory (LSTM) is proposed to predict time-series data with high noise. First, a one-dimensional convolution is used in the encoding and decoding network of the autoEncoder to extract data features and then use Long Short-Term Memory(LSTM) to predict. The experimental results show that the prediction error of convolutional autoEncoder-Long Short Term Memory (CAE and LSTM) model is significantly lower than other models.