Many data sets contain date-time fields which we hope will provide predictive value in our models. But date-time fields in the form of MM-DD-YYY HH:SS are essentially unique data points. In addition, the order in which events occur may have a bearing on outcomes.
Date-time fields can be separated into component 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. (Squark automatically factors date-time fields before model building and ranking.)
Models that consider the sequence in which events occur are called time series analytics. This technique is essential to account for factors such as seasonality, weather conditions, and economic indicators. Sales forecasts and marketing projections are classic use cases for time series forecasting.
As always, get in touch if you have questions about using date-time in your predictions, or any other Machine Learning topic. We’re happy to help.
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