Servicenow Canada Inc.

Servicenow, Inc.

Dmitri Carpov

Boris Oreshkin

Nicolas Chapados

Daniel Wong

A method for forecasting future values of a target variable using past values thereof, the values of the target variable being affected by one or more covariates wherein the covariates are independent from the target variable. The method comprises using a covariate-specific AI model, computing a covariate effect of the covariates on the target variable. The covariates effect is a defined modification to the values of the target variable caused by the covariates. The method also comprises computing intrinsic past values of the target variable by removing the covariate effect of the covariates from past values of the target variable. The method further comprises using a target-variable-specific AI model, generating an intrinsic forecast of the future values of the target variable; and computing a forecast that includes the covariate effect using the intrinsic forecast of the future values of the target variable and the covariate effect.

Covariate processing with neural network execution blocks

Systems and components for use with neural networks. An execution block and a system architecture using that execution block are disclosed. The execution block uses a fully connected stack of layers and one output is a forecast for a time series while another output is a backcast that can be used to determine a residual from the input to the execution block. The execution block uses a waveform generator sub-unit whose parameters can be judiciously selected to thereby constrain the possible set of waveforms generated. By doing so, the execution block specializes its function. The system using the execution block has been shown to be better than the state of the art in providing solutions to the time series problem.

Neural network execution block using fully connected layers

Systems, architectures, and approaches for use with neural networks. An execution block and a system architecture using a novel execution block are disclosed along with how such an execution block can be used. The execution block uses a fully connected stack of layers and parameters of this fully connected stack of layers are shared. The fully connected nature of the block and on-the-fly generated parameters allow for bypassing specialized training data sets. The system may be trained using non-task-specific training data sets and this allows the system to transfer what is learned to execute a different task. Thus, instead of having to obtain a specialized training data set for a specific task, a more generic training data set can be used to train and prepare the system for that specific task. Results have shown that performance is as good as than the state of the art in providing solutions.

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Montreal, Canada

Dmitri Carpov