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：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.

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：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：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.

公开(公告)号：US20250094861A1

公开(公告)日：2025-03-20

申请号：US18470220

申请日：2023-09-19

Applicant: Oracle International Corporation

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

IPC: G06N20/00

Abstract: Techniques for time-bound hyperparameter tuning are disclosed. The techniques enable the determination of optimized hyperparameters for a machine learning (ML) model given a specified time bound using a three-stage approach. A series of trials are executed, during each of which the ML model is trained using a distinct set of hyperparameters. In the first stage, a small number of trials are executed to initialize the algorithm. In the second and third stages, a certain number of trials are executed in each stage. The number of trials to run in each stage are determined using one or more computer-implemented techniques. The computer-implemented techniques can also be used to narrow the hyperparameter search space and the feature space. Following the third stage, a set of optimized hyperparameters is adopted based a predefined optimization criterion like minimization of an error function.

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.