Multiple Stock Time Series Jointly Forecasting with Multi-Task Learning

Due to the strong connections among stocks, the information valuable for forecasting is not only included in individual stocks, but also included in the stocks related to them. These inter-correlations can provide invaluable information to be further leveraged to improve the overall forecasting performances. However, most previous works focus on the forecasting task of one single stock, which easily ignore the valuable information in others. Therefore, in this paper, we propose a jointly forecasting approach to process the time series of multiple related stocks simultaneously, using multi-task learning framework. In particular, this framework processes multiple forecasting tasks of different stocks simultaneously by sharing the information extracted based on latent inter-correlations. Meanwhile, each stock has their private encoding networks to keep their own information. Moreover, to dynamically balance private and shared information, we propose an attention based method, called Shared-private Attention, to optimally combine the shared and private information of stocks, which is inspired by the idea of Capital Asset Pricing Model (CAPM). Experimental results on the datasets of both stock and other domains demonstrate the proposed method can outperform other methods in forecasting performance.