深度卷积记忆网络时空数据模型

Spatio-temporal data is a data type that contains temporal and spatial attributes. Training spatio-temporal data needs spatio-temporal models to deal with the relationship between data and those two attributes, to get the trend as it changes with time and space. Traffic information data is a typical type of spatio-temporal data. Due to the complexity and variability of the traffic network, and the strong coupling with time and space, traditional system simulation and data analysis methods cannot effectively obtain the relationship between data. By dealing with the adjacent spatial attribute information in traffic data, this paper designs a new spatio-temporal model, DSTCL (deep spatio-temporal convolutional long-short memory network), to solve the problem that traditional spatio-temporal models only pay attention to the temporal attribute and these models have insufficient ability to predict short-term data, and then improve the ability to predict future information. DSTCL is a multi-network structure consisting of a convolutional neural network and a long short-term memory. It can extract the temporal and spatial attribute information of the data, and correct the network by adding the periodic feature extraction module and the mirror feature extraction module. By verifying the two types of typical spatio-temporal datasets, it is shown that when DSTCL predicts the short-term information, compared with the traditional spatio-temporal models, not only the prediction error is greatly reduced, but also the training speed has been improved.