Data Transformations

A number of data transformation procedures that are provided in the Core system are useful in time series analysis. These transformations apply only to column-based data, where each time series field contains the data for a single time series.

• The Define Dates procedure (on the Data menu) generates date variables that are used to establish periodicity and to distinguish between historical, validation, and forecasting periods. Forecasting is designed to work with the variables created by the Define Dates procedure.
• The Create Time Series procedure (on the Transform menu) creates new time series variables as functions of existing time series variables. It includes functions that use neighboring observations for smoothing, averaging, and differencing.
• The Replace Missing Values procedure (on the Transform menu) replaces system- and user-missing values with estimates based on one of several methods. Missing data at the beginning or end of a series pose no particular problem; they simply shorten the useful length of the series. Gaps in the middle of a series (embedded missing data) can be a much more serious problem.