Embedding calendar knowledge in event-driven inventory forecasting
AssigneeYahoo! Inc.
InventorsKonstantin Salomatin, Datong Chen, Qi He, Peiji Chen
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Abstract
Systems and methods for automatically forecasting the future availability of one or more resources, such as Internet advertising opportunities, are described herein. In accordance with various embodiments, a forecasting model that accounts for event-driven resource availability is trained based both on historical supply data and calendar information specifying events and event duration. The trained forecasting model is then used to forecast the availability of resources at one or more specified future time periods. In accordance with certain embodiments, the forecasting model comprises a Gaussian process model that has an event-driven kernel as a covariance function.
Claims20
A method for forecasting an availability of resources, comprising:
- training, by a processor, a non-parametric forecasting model based both on historical supply data and calendar information,
- wherein the historical supply data specifies one or more past time periods in a series of time periods and a number of resources that were available during each of the one or more past time periods,
- wherein the calendar information identifies one or more events and, for each event, one or more corresponding time periods in the series of time periods during which the event occurs, thereby producing a trained forecasting model that accounts for event-driven resource availability,
- wherein the training of the non-parametric forecasting model includes training of a Gaussian process model,
- wherein the training of the Gaussian process model includes training a Gaussian model with an event-driven kernel as a covariance, and
- wherein the event-driven kernel includes a distance function that defines a de-correlation level between a calendar feature vector associated with the historical supply data and a calendar feature vector associated with an event type.
- receiving, at the processor, a specification of one or more future time periods in the series of time periods;
- and
- generating, by the processor, a forecasted number of resources available for each of the one or more future time periods based on the trained non-parametric forecasting model.
An inventory forecasting system, comprising:
- a software-implemented training module executing on one or more processor-based computer systems that trains a non-parametric forecasting model based both on historical supply data and calendar information, the historical supply data specifying one or more past time periods in a series of time periods and a number of resources that were available during each of the one or more past time periods, the calendar information identifying one or more events and, for each event, one or more corresponding time periods in the series of time periods during which the event occurs, thereby producing a trained non-parametric forecasting model that accounts for event-driven resource availability, wherein the software implement training module trains non-parametric forecasting model by training a Gaussian process model with an event-driven kernel as a covariance, and wherein a definition of the event driven kernel includes a distance function that defines a de-correlation level between a calendar feature vector associated with the historical supply data and a calendar feature vector associated with an event type;
- and
- a software-implemented forecasting module executing on the one or more processor-based computer systems that receives a specification of one or more future time periods in the series of time periods and generates a forecasted number of resources available for each of the one or more future time periods based on the trained non-parametric forecasting model.
A computer program product comprising a non-transitory computer-readable storage medium having computer program logic recorded thereon for enabling a processing unit to forecast an availability of resources, the computer program logic comprising:
- first computer program logic that is executable by the processing unit to receive a specification of one or more future time periods in a series of time periods;
- and
- second computer program logic that is executable by the processing unit to generate a forecasted number of resources available for each of the one or more future time periods based on a non-parametric forecasting model,
- wherein the non-parametric forecasting model is trained based both on historical supply data and calendar information, the historical supply data specifying one or more past time periods in the series of time periods and a number of resources that were available during each of the one or more past time periods and the calendar information identifying one or more events and, for each event, one or more corresponding time periods in the series of time periods during which the event occurs, and
- wherein the non-parametric forecasting model is trained using a Gaussian Process Model with an event-driven kernel as a covariance and the event driven kernel includes a distance function that defines a de-correlation level between a calendar feature vector associated with the historical supply data and a calendar feature vector associated with an event type.
Dependencies are inferred from the claim text.