Demand shock detection for dynamic demand forecasting

A system comprising:

• one or more processors;
• at least one memory device coupled with the one or more processors;
• and
• a data communications interface operably associated with the one or more processors, wherein the at least one memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the system to:
• generate predicted booking observations with a demand model trained using a training set of historical booking data;
• obtain transient booking observations from an active database;
• compute an observed likelihood score from the transient booking observations based on the demand model trained on the historical booking data;
• compute a demand shock threshold based on a statistical relationship between a time to detection of a demand shock event and at least one shock detection criterion, wherein the demand shock threshold is computed based on a distribution of likelihood scores from simulated instances of the transient booking observations;
• and
• determine an occurrence of a demand shock event by comparing the observed likelihood score to the demand shock threshold.

A computer program product comprising:

• a non-transitory computer-readable storage medium;
• and
• program code stored on the non-transitory computer-readable storage medium that, when executed by one or more processors, causes the one or more processors to:
• generate predicted booking observations with a demand model trained using a training set of historical booking data;
• obtain transient booking observations from an active database;
• compute an observed likelihood score from the transient booking observations based on the demand model trained on the historical booking data;
• compute a demand shock threshold based on a statistical relationship between a time to detection of a demand shock event and at least one shock detection criterion, wherein the demand shock threshold is computed based on a distribution of likelihood scores from simulated instances of the transient booking observations;
• and
• determine an occurrence of a demand shock event by comparing the observed likelihood score to the demand shock threshold.