Application and challenges of informer model in financial time series prediction: A review

Abstract

Time series prediction has shown excellent performance in various fields in recent years, such as stock prices, weather changes, traffic flow, and other fields. Its application development is becoming increasingly mature, and long series time prediction area of research has gained significant prominence. The excellent performance of deep learning in many models has unleashed the potential and possibility of time series prediction to a certain extent. Based on the above reasons, applying deep learning to the field of time series prediction has become a meaningful research. Therefore, the purpose of this article is to analyze the Informer algorithm model in the area of financial time series prediction and provide a comprehensive literature review on the implementation of Informer models in financial time series. Attempting to investigate and analyze the problems and challenges that Informer models may encounter in the area of financial time series prediction, with the hope of providing innovative inspiration and motivating new forms of knowledge for future workers.