A review of the application of deep learning to time series prediction

Abstract: With the rapid development of sensor and network technology, a large amount of historical time series data appears, so it is more and more important to predict time series efficiently and accurately. In recent years, the methods of applying deep learning ideas and techniques to time series prediction tasks have developed rapidly and achieved many results. This paper analyzes the research status of time series forecasting methods at home and abroad, discusses the relevant theories involved in time series forecasting, summarizes the traditional methods used in this task, the methods based on machine learning and the methods based on deep learning, and focuses on the comparison and analysis of the advantages and disadvantages of each method based on deep learning. Then, the prediction methods of time series based on deep learning are forecasted.