公开(公告)号：US10748072B1

公开(公告)日：2020-08-18

申请号：US15153713

申请日：2016-05-12

Applicant: Amazon Technologies, Inc.

Inventor： Matthias Seeger ,  Gregory Michael Duncan ,  Jan Alexander Gasthaus

IPC: G06N7/00 ,  G06N20/00

Abstract: 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.

公开(公告)号：US10318874B1

公开(公告)日：2019-06-11

申请号：US14662021

申请日：2015-03-18

Applicant: Amazon Technologies, Inc.

Inventor： Gregory Michael Duncan ,  Ramesh Natarajan

IPC: G06N5/04 ,  G06N7/00 ,  G06N20/00

Abstract: 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.