CNN-LSTM-VAE based time series trend prediction

Abstract

In the context of mobile Internet, time series analysis has become an important way to capture the characteristics of data such as periodicity and correlation. Establishing a temporal sequence analysis model as an effective means to capture data features, for the problems of irregularity, nonlinearity, and inconspicuous feature relationships that commonly occur in sequences. In this paper, we use convolutional neural network to extract the potential features in the sequence, and combine the long and short term memory network to analyze the temporal features in the data; meanwhile, due to the "gate" structure of the long and short term memory network, some noise in the data is introduced into the model for training, resulting in the overfitting problem. -The decode-reconstruction network structure is used to remove this noise and improve the accuracy of the model. In this paper, we use the stock data of CBS as an example and compare it with the existing algorithm model, based on which we demonstrate the higher accuracy of this algorithm with different domain data sets.