Prediction for Time Series with CNN and LSTM

Abstract

Time series data exist in various systems and affect the following management and control, in which real time series data sets are often composed of multiple variables. For predicting the future of data, not only the historical value of the variable but also other implicit influence factors should be considered. Therefore, we propose a prediction method based on the convolutional neural network (CNN) and Bi-directional long short term memory (Bi-LSTM) networks with the multidimensional variable. CNN is used to learn the horizontal relationship between variables of multivariate raw data, and Bi-LSTM is used to extract temporal relationships. Experiments are carried out with Beijing meteorological data, and the results show the high prediction accuracy of wind speed and temperature data. It is indicated that the proposed model can explore effectively the features of multivariable non-stationary time series data.