Abstract: Computer-implemented systems and methods for providing a forecast using time series data that is indicative of a data generation activity occurring over a period of time. Candidate models and candidate input variables are received. For each candidate model, transfer functions are determined for the candidate input variables in order to relate a variable to be forecasted to the time series data. For each candidate model there is a selection of which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions. A model is selected from the candidate models to forecast the time series data using the selected input variables of the selected model.

Abstract: Computer-implemented systems and methods for performing forecast scoring based upon time series data that is indicative of a data generation activity occurring over a period of time. One or more data stores store historical data and future data. The historical data includes historical exogenous inputs. The future data includes future exogenous inputs. A dynamic statistical model receives the historical data and future data that are stored in the one or more data stores. The dynamic statistical model is to provide forecast scoring based upon the historical exogenous inputs and the future exogenous inputs. An optimizer having an objective function iteratively varies the future data, so that forecasts associated with the varying future data values are generated by the dynamic statistical model for evaluation by the objective function.