DeepAR-Attention probabilistic prediction for stock price series

Jiacheng Li, Wei Chen, Zhiheng Zhou, Junmei Yang, Delu Zeng

Abstract: Stock price prediction is a significant research domain, intersecting statistics, finance, and economics. Accurately forecasting stock price trends has always been a focal point for many researchers. However, traditional statistical methods for time series prediction still lack accuracy. The existing deep learning-based methods for stock price prediction have significantly enhanced the accuracy of predicting individual stock prices. However, they are not effective in forecasting the probability range of future stock price trends. In this paper, to address these limitations, we propose a novel DeepAR model based on the attention mechanism (DeepARA) for both single-point and probabilistic predictions of stock prices. This enhances the accuracy and flexibility of stock price forecasting. Although the attention mechanism was initially developed for natural language processing, it has now found applications in time series forecasting, including the dynamics of the stock market. Attention allocates different weights to time points of varying importance, thereby enhancing the model’s ability to capture fundamental market dynamics. We conducted multiple experiments in the Chinese stock market, involving 30 stocks across the top six sectors. Compared with baseline models, the DeepARA model demonstrates superior predictive capabilities.