Systems and methods for predicting service demand based on geographically associated events

A method of predicting event an impact of one or more events on service demand, comprising:

• a. receiving a first set of data comprising properties of one or more historic events, each having at least a temporal factor;
• b. characterising one or more properties of each of the one or more historic events into a plurality of metadata tags;
• c. receiving a second set of data relating to a demand for one or more services, the demand for each of the one or more services having at least a temporal factor;
• d. filtering the demand for the one or more services to thereby distinguish ordinary demand from extra-ordinary demand;
• e. determining, by machine learning, one or more correlations between two or more of the plurality of metadata tags, or combinations thereof and extra-ordinary demand, then quantifying the impact of the correlation with at least a binary characterisation;
• producing a third data set comprising a grouping of two or more of the metadata tags, or combinations thereof, associated with events having impact quantified above a threshold, wherein the third dataset is operable for predictive determinations of future event impact on service demand;
• and
• g. determining, by machine learning, a prediction model relating two or more of the metadata tags, or combinations thereof of the third data set to extraordinary demand.

A system for controlling at least one of price or capacity of a service offering, the system comprising:

• one or more databases that store:
• a. a first set of data comprising properties of one or more historic events, each event having at least a temporal factor;
• and
• b. a second set of data relating to a demand for one or more services located, the demand for services having at least a temporal factor;
• c. a service data set relating to at least one of a price or a capacity of at least one service product of
• d. a third data set comprising one or more metadata tags characterising a future event, and an analysis unit configured to:
• e. characterise properties of each of the one or more historic events into the one or more metadata tags;
• f. filter the demand for the one or more services to thereby distinguish ordinary demand from extra-ordinary demand;
• g. determine, by machine learning, a correlation between the one or more metadata tags, or combinations thereof, and extra-ordinary demand, and quantifying the impact of the correlation, with at least a binary characterisation;
• h. store a third data set in a further database, the third dataset comprising a grouping of the one or more metadata tags, or combinations thereof, associated with events having impact quantified above a threshold, wherein the third dataset is operable for predictive determinations of future event impact on service demand;
• i. determine, by machine learning, a prediction model relating the one or more metadata tags, or combinations thereof of the third data set to extra-ordinary demand;
• j. receive an event data set comprising one or more of the metadata tags characterising a future event;
• an
• k. calculate the predicted measure of impact of the future event on the service by application of the prediction model to the future event third data set;
• then
• l. alter the service provider capacity based on the calculated measure of impact.

A system for predicting event impact on service demand, comprising:

• one or more databases that store:
• a. a first set of data comprising properties of one or more historic events, each event having at least a temporal factor;
• and
• b. a second set of data relating to a demand for one or more services, the demand for services having at least a temporal factor;
• and
• an analysis unit configured to:
• c. characterise properties of each of the one or more historic events into one or more metadata tags;
• d. filters the demand for one or more services to thereby distinguish ordinary demand from extra-ordinary demand;
• e. determine, by machine learning, a correlation between the one or more metadata tags, or combinations thereof, and extra-ordinary demand, then quantifying the impact of the correlation at least by a binary characterisation;
• f. store a third data set in a further database, the third dataset comprising a grouping of the one or more metadata tags, or combinations thereof, associated with events having impact quantified above a threshold, wherein the third dataset is operable for predictive determinations of future event impact on the demand of one or more of the services;
• and
• g. determine, by machine learning, a prediction model relating one or more of the metadata tags, or combinations thereof, of the third data set to extra-ordinary demand.

A non-transitory computer-readable storage medium storing instructions that, when executed by a computer process, cause a computing device to:

• receive a first set of data comprising properties of one or more historic events, each having at least a temporal factor;
• characterise one or more properties of each of the one or more historic events into one or more metadata tags:
• receive a second set of data relating to a demand for one or more services, having at least a temporal factor;
• filter the demand for the one or more services to thereby distinguish ordinary demand from extra-ordinary demand;
• determine by machine learning, a correlation between the one or more metadata tags, or combinations thereof, and extra-ordinary demand, then quantifying the impact of the correlation at least by a binary characterisation;
• produce a third data set comprising a grouping of the one or more metadata tags, or combinations thereof, associated with events having impact quantified above a threshold, wherein the third dataset is operable for predictive determinations of future event impact on service demand;
• and
• determine, by machine learning, a prediction model relating the one or more metadata tags, or combinations thereof of the third data set to extra-ordinary demand.