Date factoring is a feature engineering technique that splits date-time data into its component parts. For instance, a date-time field with a format of MM-DD-YYY HH:SS can be separated into variables of Month, Date, Year, Time, Day of Month, Day of Week, and Day of Year. Pre-processing data sets to add columns for these individual variables may add predictive value when building models.
Where the sequence in which events occur is important, regression models that forecast values based solely on discrete date/time factors may not provide useful predictions. Sales forecasting or market projections are classic examples. See Time-series Forecasting.