Abstract: Systems and methods to generate predicted variances of an operation based on data from one or more connected databases.

Abstract: Systems and methods to generate predicted variances of an operation based on data from one or more connected databases.

Abstract: Systems and methods to generate predicted variances of an operation based on data from one or more connected databases.

Abstract: A system is disclosed for determining a real-time probability iteratively throughout a period of time. The system conducts an upgrade analysis by determining that an expected marginal seat revenue for a sale of an additional coach seat is greater than an expected marginal seat revenue for a sale of an additional first class seat.

Abstract: The system applies logistic regression to historical data to generate forecast coefficients. The system creates an updated authorization parameter in real-time iteratively throughout a period of time.

Abstract: A system is disclosed for forecasting costs and determining probabilities, along with the use of prediction modeling. The system analyzes previous actions to determine future actions, while using cost prediction modeling.

Abstract: The system applies logistic regression to historical data to generate forecast coefficients. The system creates an updated authorization parameter in real-time iteratively throughout a period of time.

Abstract: A system is disclosed for forecasting costs and determining probabilities, along with the use of prediction modeling. The system analyzes previous actions to determine future actions, while using cost prediction modeling.

Abstract: A system is disclosed for determining a real-time probability iteratively throughout a period of time. The system conducts an upgrade analysis by determining that an expected marginal seat revenue for a sale of an additional coach seat is greater than an expected marginal seat revenue for a sale of an additional first class seat.

Abstract: The system applies logistic regression to historical data to generate forecast coefficients. The system creates an updated authorization parameter in real-time iteratively throughout a period of time.

Abstract: A system is disclosed for forecasting costs and determining probabilities, along with the use of prediction modeling. The system analyzes previous actions to determine future actions, while using cost prediction modeling.

Abstract: A system is disclosed for determining a real-time probability iteratively throughout a period of time. The system conducts an upgrade analysis by determining that an expected marginal seat revenue for a sale of an additional coach seat is greater than an expected marginal seat revenue for a sale of an additional first class seat.

Abstract: A system for linear regression modeling for load factors is disclosed. The system determines capacity, optimal authorizations, probabilities and models using a real-time iterative process throughout a period of time.

Abstract: A computer based system for maximizing revenue by determining an optimal quantity of a product to be sold is disclosed. The system determines the optimal number of seats to be sold for a flight based upon the flight's capacity and forecasted costs associated with the flight. The forecasting is based upon probabilistic distribution models takes into account passenger itinerary data, passenger and market historical data, whether a passenger has flown on a previous leg of an itinerary, the ripple denied boarding effect of reaccommodating a denied passenger. The system evaluates the potential effect of double selling a unit of inventory (e.g., seats). Downstream inventory control, revenue management and reservations systems may use the optimization data to affect the operation of the airline.

Abstract: A computer based system for maximizing revenue by determining an optimal quantity of a product to be sold is disclosed. The system determines the optimal number of seats to be sold for a flight based upon the flight's capacity and forecasted costs associated with the flight. The forecasting is based upon probabilistic distribution models and takes into account passenger itinerary data, passenger and market historical data, whether a passenger has flown on a previous leg of an itinerary, and the ripple denied boarding effect of reaccommodating a denied passenger. The system evaluates the potential effect of double selling a unit of inventory (e.g., seats). Downstreanm inventory control, revenue management and reservations systems may use the optimization data to affect the operation of the airline.

Abstract: A computer based system for maximizing revenue by determining an optimal quantity of a product to be sold is disclosed. The system determines the optimal number of seats to be sold for a flight based upon the flight's capacity and forecasted costs associated with the flight. The forecasting is based upon probabilistic distribution models and takes into account passenger itinerary data, passenger and market historical data, whether a passenger has flown on a previous leg of an itinerary, and the ripple denied boarding effect of reaccommodating a denied passenger. The system evaluates the potential effect of double selling a unit of inventory (e.g., seats). Downstream inventory control, revenue management and reservations systems may use the optimization data to affect the operation of the airline.

Abstract: A computer based system for maximizing revenue by determining an optimal quantity of a product to be sold is disclosed. The system determines the optimal number of seats to be sold for a flight based upon the flight's capacity and forecasted costs associated with the flight. The forecasting is based upon probabilistic distribution models takes into account passenger itinerary data, passenger and market historical data, whether a passenger has flown on a previous leg of an itinerary, the ripple denied boarding effect of reaccommodating a denied passenger. The system evaluates the potential effect of double selling a unit of inventory (e.g., seats). Downstream inventory control, revenue management and reservations systems may use the optimization data to affect the operation of the airline.

Abstract: A computer based system for maximizing revenue by determining an optimal quantity of a product to be sold is disclosed. The system determines the optimal number of seats to be sold for a flight based upon the flight's capacity and forecasted costs associated with the flight. The forecasting is based upon probabilistic distribution models takes into account passenger itinerary data, passenger and market historical data, whether a passenger has flown on a previous leg of an itinerary, the ripple denied boarding effect of reaccommodating a denied passenger. The system evaluates the potential effect of double selling a unit of inventory (e.g., seats). Downstream inventory control, revenue management and reservations systems may use the optimization data to affect the operation of the airline.

Abstract: A computer based system for maximizing revenue by determining an optimal quantity of a product to be sold is disclosed. The system determines the optimal number of seats to be sold for a flight based upon the flight's capacity and forecasted costs associated with the flight. The forecasting is based upon probabilistic distribution models takes into account passenger itinerary data, passenger and market historical data, whether a passenger has flown on a previous leg of an itinerary, the ripple denied boarding effect of reaccommodating a denied passenger. The system evaluates the potential effect of double selling a unit of inventory (e.g., seats), Downstream inventory control, revenue management and reservations systems may use the optimization data to affect the operation of the airline.

Abstract: A computer based system for maximizing revenue by determining an optimal quantity of a product to be sold is disclosed. The system determines the optimal number of seats to be sold for a flight based upon the flight's capacity and forecasted costs associated with the flight. The forecasting is based upon probabilistic distribution models takes into account passenger itinerary data, passenger and market historical data, whether a passenger has flown on a previous leg of an itinerary, the ripple denied boarding effect of reaccommodating a denied passenger. The system evaluates the potential effect of double selling a unit of inventory (e.g., seats). Downstream inventory control, revenue management and reservations systems may use the optimization data to affect the operation of the airline.