Amazon Technologies, Inc.

Gregory Michael Duncan

Ramesh Natarajan

Jan Alexander Gasthaus

Matthias Seeger

With respect to an input data set which contains observation records of a time series, a statistical model which utilizes a likelihood function comprising a latent function is generated. The latent function comprises a combination of a deterministic component and a random process. Parameters of the model are fitted using approximate Bayesian inference, and the model is used to generate probabilistic forecasts corresponding to the input data set.

Intermittent demand forecasting for large inventories

Corresponding to each forecasting model of a family of related models for a time series sequence, a respective state space representation is generated. One or more cross-validation iterations are then executed for each model of the family. In a given iteration, a training variant of the time series sequence is generated, with a subset of the time series sequence entries replaced by representations of missing values. Predictions for the missing values are obtained using the state space representation and the training variant, and a model quality metric is obtained based on prediction errors. The optimal model of the family is selected using the model quality metrics obtained from the cross validation iterations.

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Bellevue, WA, United States of America

Gregory Michael Duncan