公开(公告)号：US20240386047A1

公开(公告)日：2024-11-21

申请号：US18198975

申请日：2023-05-18

Applicant: Oracle International Corporation

Inventor： Ankit Aggarwal ,  Chirag Ahuja ,  Vikas Pandey ,  Sharmily Sidhartha ,  Hariharan Balasubramanian ,  Jie Xing

IPC: G06F16/38 ,  G06F16/31

Abstract: Techniques are described herein for cold-start forecasting datasets using backcasting and composite embedding. An example method can include a system receiving a set of time series and metadata text comprising a first subset of metadata text and a second subset of metadata text. The system can generate a plurality of embeddings, each embedding comprising a numerical representation of a metadata text of the set of metadata text. The system can generate a plurality of vectors, each vector comprising a time series of the set of time series each time series associated with a metadata text of the first subset of metadata text. The system can generate a plurality of composite embeddings based at least in part on combining each embedding with a respective vector of the plurality of vectors. The system can determine a forecasted value associated with the second subset of metadata text based on the composite embeddings.

公开(公告)号：US20240362210A1

公开(公告)日：2024-10-31

申请号：US18139492

申请日：2023-04-26

Applicant: Oracle International Corporation

Inventor： Ankit Aggarwal ,  Jie Xing ,  Chirag Ahuja ,  Vikas Pandey ,  Hariharan Balasubramanian

IPC: G06F16/242 ,  G06F16/2455

CPC classification number: G06F16/244 ,  G06F16/24553

Abstract: Techniques are described herein for forecasting datasets using blend of temporal aggregation and grouped aggregation. An example method can include a device accessing a first and second time series, comprising a first data point associated with a first time step and a first value and a second data point associated with a second time step and a second value. The method can further include the device determining a grouped aggregated data point using the first and second time series by aligning the first and second data point. The method can further include the device determining the grouped aggregated data point by summing the first and second value. The method can further include determining a grouped aggregated time series. The method can further include the device determining a first set of input values for a machine learning model. The method can further include the device determining a first forecasted future value.

公开(公告)号：US20240362517A1

公开(公告)日：2024-10-31

申请号：US18138930

申请日：2023-04-25

Applicant: Oracle International Corporation

Inventor： Ankit Aggarwal ,  Chirag Ahuja ,  Jie Xing ,  Michal Piotr Prussak

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Techniques described herein are directed toward univariate series truncation policy using change point detection. An example method can include a device determining a first time series comprising a first set of data points indexed over time. The device can determine a first and second change point of the first time series based on a relative position and a category of the change points. The device can generate a first and second truncated time series based on the change points. The device can generate a first and second forecasted value using a first forecasting technique. The device can compare the first forecasted value and the second forecasted value using a second time series. The device can select one of the forecasting techniques to generate a final forecasted value based on the comparison. The device can generate, using the selected first forecasting technique, the final forecasted value.