My case is the following, i am trying to apply UCM on a time series containing volume as Y and a few promotion variables as X1-X....
The problem i have is that the series is not complete meaning , there can be a gap between 4July and 18july (11july missing) as well as larger gaps for example between 19september and 19december where all info or dates between them are missing. Since my goal is to forecast shall I:
1. perhaps drop UCM (since an id error appears due to the gaps) ?
2. Is there any other modelling technique to overcome that problem?
All time series based forecasting methods have a similar requirement for the ID variable (date or datetime information): it needs to be complete, with other words missing values in the time ID are not allowed. As such you will have to fill in these gaps for the time ID - for example by using PROC TIMESERIES:
if date="01JAN57"d then delete;
if date="01SEP59"d then delete;
proc timeseries data=have out=want;
id date interval=month accumulate=total;
One the time id is intact, for UCM the following holds true (from documentation): "Embedded missing values in the dependent variable usually cause no problems in UCM modeling. However, no embedded missing values are allowed in the predictor variables."
Depending on your business question at hand you may want to impute missing values of the dependent varialbe using PROC TIMESERIES for example (see setmissing option).
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