公开(公告)号：US20230113287A1

公开(公告)日：2023-04-13

申请号：US17694323

申请日：2022-03-14

Applicant: Oracle International Corporation

Inventor： Ankit Kumar Aggarwal ,  Anku Kumar Pandey ,  Ravijeet Ranjit Kumar ,  Samik Raychaudhuri

IPC: G06K9/62 ,  G06N20/20

Abstract: A time series forecasting service system is disclosed. The system identifies a set of cross-validation parameters to be used for cross-validating a model to be used for generating a requested forecast. The requested forecast includes a time series dataset and a forecast horizon identifying a number of time steps for which a forecast is to be made using the time series dataset. The system identifies an objective function to be minimized for determining optimal values for the set of cross-validation parameters and identifies constraints for the cross-validation parameters. The system uses an optimization technique to determine the optimal values for the cross-validation parameters. The optimization technique performs processing that determines the optimal values by minimizing the objective function while satisfying the set of constraints. The system uses the optimal values for the cross-validation parameters to perform cross-validation of the model to be used for making the requested forecast.

公开(公告)号：US20230123573A1

公开(公告)日：2023-04-20

申请号：US17861634

申请日：2022-07-11

Applicant: Oracle International Corporation

Inventor： Chirag Ahuja ,  Samik Raychaudhuri ,  Anku Kumar Pandey ,  Nitin Rawat

IPC: G06F17/18 ,  G06F16/2458 ,  G06F17/14

Abstract: The present embodiments relate to generating input parameters for selecting a forecasting model. An example method includes a computing device receiving a time series comprising a plurality of data points, wherein each data point of the time series comprises a time associated with the data point and a value. The device can identify a first season and a second season from the time series, wherein a length of the first season is a factor of a length of the second season. The device can estimate a Fourier order and a seasonality mode for the first season based at least in part on the length of the first season and the length of the second season. The device can select a forecasting model to forecast a value of a future time step of the time series based at least in part on the Fourier order and the seasonality mode.