Abstract: Temporal budget optimization in online advertising, comprising: receiving a user selection of a time period in the future and of a joint budget for M online ad entities, wherein M?2; forecasting, based on historical data associated with the M online ad entities, a future ROI function of each of the M online ad entities, wherein the future ROI function provides revenue as a function of cost; computing individual budgets for the M online ad entities by finding M points to serve as the individual budgets, each of the M points being a certain cost in the future ROI function of a different one of the M online ad entities, such that: the M points have approximately equal derivatives, and a sum of the costs at the M points is approximately equal to the joint budget; and during the time period: tracking a spending of the individual budgets, to determine remaining individual budgets, periodically updating the future ROI functions based on newly-accumulated historical data associated with the M online ad entities, and

Abstract: A method for temporal budget optimization in online advertising, the method comprising using at least one hardware processor for: receiving a user selection of a time period in the future; forecasting, based on historical data associated with an online ad entity, a future return on investment (ROI) function of the online ad entity; receiving a user selection of a point on the ROI function, thereby setting a budget for the time period; and during the time period: (a) tracking a spending of the budget, to determine a remaining budget, (b) periodically updating the future ROI function based on newly-accumulated historical data associated with the online ad entity, and (c) periodically adjusting, in an online advertising platform, a spending pace of the remaining budget, wherein the adjusting is based on the updated future ROI function.

Abstract: Computer implemented method and system for multi-server computing of demand forecast information for a demand forecast tree to shorten run time.