Integrating scheduled future events into training of time series forecasting model
AssigneeSage Global Services Limited
InventorTomonori Masui
Life of this patent
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Abstract
In various embodiments, a time series forecasting model can take into account scheduled (or high-confidence) future events into training of a time series forecasting model. During model training, such future event data can be integrated with data describing past events, so as to improve the accuracy of the model in generating forecasts. By taking into account such scheduled (or high-confidence) future events, forecasting of unscheduled future events may be improved.
Claims21
A computer-implemented method for generating forecasts of unscheduled future events, comprising:
- at a processor, obtaining training data for a predictive model, wherein the training data comprises:
- at least one past event having an event occurrence time prior to the present time;
- and
- and at least one high-confidence future event, each comprising a previously scheduled event having an event occurrence time subsequent to the present time;
- at the processor, generating a two-dimensional feature matrix from the training data, wherein:
- a first dimension of the feature matrix represents event occurrence time;
- and
- a second dimension of the feature matrix represents time at which each event is scheduled;
- at the processor, training the predictive model using the generated feature matrix;
- at the processor, applying the trained model to generate forecasts of unscheduled future events;
- and
- at an output device, outputting the forecasts of unscheduled future events.
A non-transitory computer-readable medium for generating forecasts of unscheduled future events, comprising instructions stored thereon, that when performed by a hardware processor, perform the steps of:
- obtaining training data for a predictive model, wherein the training data comprises:
- at least one past event having an event occurrence time prior to the present time;
- and
- and at least one high-confidence future event, each comprising a previously scheduled event having an event occurrence time subsequent to the present time;
- generating a two-dimensional feature matrix from the training data, wherein:
- a first dimension of the feature matrix represents event occurrence time;
- and
- a second dimension of the feature matrix represents time at which each event is scheduled;
- training the predictive model using the generated feature matrix;
- applying the trained model to generate forecasts of unscheduled future events;
- and
- causing an output device to output the forecasts of unscheduled future events.
A system for generating forecasts of unscheduled future events, comprising:
- a hardware processor, configured to:
- obtain training data for a predictive model, wherein the training data comprises:
- at least one past event having an event occurrence time prior to the present time;
- and
- and at least one high-confidence future event, each comprising a previously scheduled event having an event occurrence time subsequent to the present time;
- generate a two-dimensional feature matrix from the training data, wherein:
- a first dimension of the feature matrix represents event occurrence time;
- and
- a second dimension of the feature matrix represents time at which each event is scheduled;
- train the predictive model using the generated feature matrix;
- and
- apply the trained model to generate forecasts of unscheduled future events;
- and
- an output device, communicatively coupled to the hardware processor, configured to output the forecasts of unscheduled future events.
Dependencies are inferred from the claim text.