Abstract: The conditional restricted Boltzmann machine time series model based on the Gaussian process(GCRBM)could efficiently predict single type of time series data, but the model could not make accurate predictions for multi-category data and real high-dimensional data. To solve the problem above, the time series prediction model based on integrated deep learning was proposed. Multiple deep belief networks(DBN)corresponding to the multi-category timing data was trained to study low dimensional feature. The low dimensional feature of multi-category data was used to train multiple GCRBM models. When the time series was predicted, the dimensionality of the model was reduced and categories of target data were identified by DBN model's reconstruction error, and the sequence of target data was predicted by the GCRBM model. The experimental results based on CASIA-A gait data set showed that the method could accurately recognize the categories of gait sequences and the predicting result could simulate the true gait sequences, which demonstrated the validity of the model.

Key words: time series, prediction model, conditional restricted Boltzmann machine(GCRBM), integrated deep learning, deep belief networks(DBN)