1. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising: receiving, using one or more processors, a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein determining transfer functions includes determining delay for each regressor; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model.
1. A computer-implemented method to provide one or more model specifications using time series data that is indicative of a data generation activity occurring over a period of time, comprising: receiving, using one or more processors, a plurality of candidate models; receiving a plurality of candidate input variables; for each candidate model, determining transfer functions for the candidate input variables in order to relate a variable to be forecasted to the time series data, wherein determining transfer functions includes determining delay for each regressor; automatically selecting for each candidate model which of the candidate input variables to include in each of the candidate models based upon the determined transfer functions; and automatically selecting a model from the candidate models to forecast the time series data using the selected candidate input variables of the selected model. 14. The method of claim 1 , further comprising: performing outlier detection with respect to each of the candidate models; for a detected outlier, creating dummy regressors for use in forecasting the time series data.