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.