Systems and methods for determining event schedules

A system comprising:

• one or more computer systems programmed to perform operations comprising:
• receiving one or more event parameters for one or more series of live events, wherein the one or more event parameters comprise scheduling information for one or more performances by one or more performers;
• receiving one or more event target features associated with the series of live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof;
• providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;
• iteratively training the ML model to identify relationships between the one or more event parameters and the one or more event target features using historical data corresponding to one or more previous series of live events, wherein such iterative training improves the accuracy of the ML model;
• receiving, from a user, one or more user-specific event parameters for a future series of live events associated with a first performer, the user-specific event parameters including scheduling information for one or more future performances by at least one second performer;
• receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the future series of live events;
• providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model;
• generating, via the trained ML model, a schedule for the future series of live events that is optimized relative to the one or more prioritized event target features;
• detecting a real-time change to the scheduling information for the one or more future performances by the at least one second performer;
• providing the real-time change to the trained ML model to improve the accuracy of the trained ML model;
• and
• updating, via the trained ML model, the schedule for the future series of live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the scheduling information for the one or more future performances by the at least one second performer.

A computer-implemented method of dynamically generating an event schedule, the method comprising:

• receiving one or more event parameters for live events comprising performances by a plurality of performers, wherein the one or more event parameters comprise scheduling information for one or more performances by one or more performers;
• receiving one or more event target features associated with live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof;
• providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;
• iteratively training the ML model to identify relationships between the one or more event parameters and the one or more event target features using historical data corresponding to a plurality of previous live events, wherein such iterative training improves the accuracy of the ML model;
• receiving, from a user, one or more user-specific event parameters for a plurality of future live events associated with a first performer, the user-specific event parameters including scheduling information for one or more future performances by at least one second performer;
• receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the plurality of future live events;
• providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model;
• generating, via the trained ML model, a schedule for the plurality of future live events that is optimized relative to the one or more prioritized event target features;
• detecting a real-time change to the scheduling information for the one or more future performances by the at least one second performer;
• providing the real-time change to the trained ML model to improve the accuracy of the trained ML model;
• and
• updating, via the trained ML model, the schedule for the plurality of future live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the scheduling information for the one or more future performances by the at least one second performer.

A system comprising:

• one or more computer systems programmed to perform operations comprising:
• receiving one or more event parameters for live events comprising performances by a plurality of performers, wherein the one or more event parameters comprise scheduling information for one or more performances by one or more performers;
• receiving one or more event target features associated with live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof;
• providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector machine ML model;
• iteratively training, in a second stage, the ML model to identify relationships between the one or more event parameters and the one or more event target features using historical data corresponding to a plurality of previous live events, wherein such iterative training improves the accuracy of the ML model;
• receiving, from a user, one or more user-specific event parameters for a plurality of future live events associated with a first performer, the user-specific event parameters including scheduling information for one or more future performances by at least one second performer;
• receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the plurality of future live events;
• providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model;
• generating, via the trained ML model, a schedule for the plurality of future live events that is optimized relative to the one or more prioritized event target features;
• detecting a real-time change to the scheduling information for the one or more future performances by the at least one second performer;
• providing the real-time change to the trained ML model to improve the accuracy of the trained ML model;
• and
• updating, via the trained ML model, the schedule for the plurality of future live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the scheduling information for the one or more future performances by the at least one second performer.