Ensemble of clustered dual-stage attention-based recurrent neural networks for multivariate time series prediction

A computer program product for multivariate time series prediction, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to perform a method comprising:

• decomposing each time series from among a batch of multiple driving time series and a target time series into a raw component, a shape component, and a trend component, the shape component including an original time series segment minus values of each time series at T time step, and the trend component including a value difference of two consecutive time steps;
• for each of the decomposed components, selecting a driving time series relevant thereto from the batch and obtaining hidden features of the selected driving time series, by applying the batch to an input attention-based encoder of an Ensemble of Clustered dual-stage attention-based Recurrent Neural Networks (EC-DARNNS);
• automatically clustering the hidden features in a hidden space using a temporal attention-based decoder of the EC-DARNNS, each of the Clustered dual-stage attention-based Recurrent Neural Networks (RNNs) in the Ensemble being dedicated and applied to a respective one of the decomposed components;
• and
• predicting a respective value of one or more future time steps for the target time series based on respective prediction outputs for each of the decomposed components by the EC-DARNNS.

A computer processing system for multivariate time series prediction, comprising:

• a memory for storing program code;
• and
• a processor operatively coupled to the memory for executing the program code to:
• decompose each time series from among a batch of multiple driving time series and a target time series into a raw component, a shape component, and a trend component, the shape component including an original time series segment minus values of each time series at T time step, and the trend component including a value difference of two consecutive time steps;
• for each of the decomposed components, select a driving time series relevant thereto from the batch and obtain hidden features of the selected driving time series, by applying the batch to an input attention-based encoder of an Ensemble of Clustered dual-stage attention-based Recurrent Neural Networks (EC-DARNNS);
• automatically cluster the hidden features in a hidden space using a temporal attention-based decoder of the EC-DARNNS, each of the Clustered dual-stage attention-based Recurrent Neural Networks (RNNs) in the Ensemble being dedicated and applied to a respective one of the decomposed components;
• and
• predict a respective value of one or more future time steps for the target time series based on respective prediction outputs for each of the decomposed components by the EC-DARNNS.