关键词: 序列预测, 社交网络, 点过程, LSTM, 时空深度网络

Abstract: Predicting the type and time of the next event according to the sequence data is a subject worth studying.At present, the point process intensity function only considers the background knowledge and historical influence from the time dimension, and has no influence on the social relations from the spatial dimension.Aiming at this problem, a sequence prediction algorithm (SPSP algorithm) is proposed based on the spatio-temporal deep network.In this model, firstly the background knowledge and historical influence of the intensity function are modeled with the dual LSTM (long short-term memory).Then the output of two LSTMs are combined by the union layer to generate the vector representation of event type and time.Finally, the influence of social networks on the spatial dimension is added to optimize the intensity function.Through multiple training of the Spatio-temporal deep neural network, the optimal network model is obtained.Sina weibo data sets are used to verify the validity of the algorithm, and it has been proven by experiments that the proposed algorithm can predict the event type and time efficiently accurately.

Key words: sequence prediction, social networking, point process, LSTM, deep time-space network