COMBINED DISCRETE AND INCREMENTAL OPTIMIZATION IN GENERATING ACTIONABLE OUTPUTS

摘要

An optimization solver divides time-indexed historical data into intervals that have temporal boundaries. A discrete coefficient evaluator calculates coefficient values in a forecasting model at the temporal boundaries of the training data. An incremental parameter evaluator evaluates incremental parameter changes between the temporal boundaries in the training data. The incremental parameter evaluator updates the parameter values, based upon the incremental changes in the parameters, so that the updated parameter values can be used by the discrete coefficient evaluator for evaluating coefficient values at a next temporal boundary. The trained forecasting modes is deployed in a system to forecast phenomena.

主权项

1. A computing system, comprising:
a time interval identifier component that accesses training data in a data store and divides the training data into corresponding time intervals and identifies a time interval, of a plurality of different time intervals, into which the training data is divided; a boundary evaluation component that is configured to evaluate coefficient values, at a boundary of the time interval, using the training data corresponding to the identified time interval and using model parameter values identified from an immediately previous time interval, for an optimization problem that trains the model parameter values for a model that models a characteristic of the training data; and an incremental parameter evaluation component that is configured to identify changes in the model parameter values during the time interval and to update the model parameter values based on the changes, the incremental parameter evaluation component providing the updated model parameter values to the boundary evaluation component for evaluation of the coefficient values at a boundary of a next subsequent time interval.