公开(公告)号：US20230297861A1

公开(公告)日：2023-09-21

申请号：US17696685

申请日：2022-03-16

Applicant: Oracle International Corporation

Inventor： Chirag Ahuja ,  Vikas Rakesh Upadhyay ,  Syed Fahad Allam Shah ,  Samik Raychaudhuri ,  Hariharan Balasubramanian ,  Michal Piotr Prussak ,  Shwan Ashrafi

IPC: G06N5/04 ,  G06F16/901

CPC classification number: G06N5/046 ,  G06F16/9024 ,  G06N20/00

Abstract: A computing device may access a graph comprising one or more model nodes, one or more dataset nodes, and one or more edges, the model nodes having a plurality of features. The device may add one or more test dataset nodes and test edges to the graph. The device may perform a series of iterative steps until a threshold is reached. For each iterative step: a selection probability is determined, the selection probability being based at least in part on a plurality of selection criteria; a particular model node is selected, the particular model node being selected based at least in part on the selection probability; the selection criteria is updated based at least in part on the particular model; and the plurality of features are updated based at least in part on the particular model. The device may provide the particular model node selected in the last iterative step.

公开(公告)号：US20230385663A1

公开(公告)日：2023-11-30

申请号：US18323339

申请日：2023-05-24

Applicant: Oracle International Corporation

Inventor： Chirag Ahuja ,  Vikas Rakesh Upadhyay ,  Samik Raychaudhuri ,  Syed Fahad Allam Shah ,  Hariharan Balasubramanian

IPC: G06N5/045

CPC classification number: G06N5/045

Abstract: A time series forecasting system is disclosed that obtains a time series forecast request requesting a forecast for a particular time point. The forecast request identifies a primary time series dataset for generating the requested forecast and a set of features related to the primary time series dataset. The system provides the primary time series dataset and the set of features to a model to be used for generating the forecast. The model computes a feature importance score for one or more features and selects a subset of features based on their feature importance scores. The model determines attention scores for a set of data points in the primary time series dataset based on the selected subset of features. The system predicts an actual forecast for the particular time point based on the attention scores and outputs the actual forecast and explanation information associated with the actual forecast.