Use PROC TIMESERIES to first fill in the gaps with missing and then use PROC EXPAND to do the moving averages.
You could also use a data step if so inclined, but this method is easier as it will scale to month/years/weeks as necessary.
Here's a fully worked example that shows how this is done at the monthly level, you will need to change the INTERVAL to day for your case.
/*this is an example of time series data.
1. Create a time series data set with missing intervals (IBM)
2. Add back missing entries using PROC TIMESERIES (IBM_NO_MISSING)
3. Calculate moving average - 12 month average
*/
/*1*/
data ibm;
set sashelp.stocks;
where stock='IBM';
if month(date)=7 then
delete;
run;
proc sort data=ibm;
by date;
run;
/*2*/
proc timeseries data=ibm out=ibm_no_missing;
id date interval=month start='01Aug1986'd end='01Dec2005'd;
var open;
run;
/*3*/
proc expand data=ibm_no_missing out=want;
id date;
convert open = open_12 / method=none transformout= (movave 12);
run;
@ebowen wrote:
I'm trying to do a moving average with PROC EXPAND where the average takes into account only those observations that occur within the previous seven calendar days, which is not necessarily the previous seven observations. I have a dataset that has some gaps, such as weekends, holidays, etc., where the data is not reported. But I don't want to average observations that are outside the weekly window. In other words, if I'm calculating the moving average on a Monday, I want the average to be only those observations from the previous Tuesday to the current day, regardless of whether there are observations for the weekend.
Currently PROC EXPAND is taking seven previous observations, which can average in numbers from a few days before that. Here's my code, data attached:
proc expand DATA=gas.gasprices OUT=gas.GasPrices_ma METHOD=none;
ID Date;
convert spread=Spread_MA / TRANSFORMOUT = (nomiss movave 7 trim 6);
run;
