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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Stock trend prediction is a challenging task due to the non-stationary dynamics and complex market dependencies. Existing methods usually regard each stock as isolated for prediction, or simply detect their correlations based on a fixed predefined graph structure. Genuinely, stock associations stem from diverse aspects, the underlying relation signals should be implicit in comprehensive graphs. On the other hand, the RNN network is mainly used to model stock historical data, while is hard todoi:10.24963/ijcai.2021/508 fatcat:6om4fintnvfkbojz6ai6jory34