Since you do not have a long history of data, I suggest to calculate forecast values using a simple method like moving average. You can use PROC ARIMA with the options: noint noest and method=CLS. See the documentation at:
http://support.sas.com/documentation/cdl/en/etsug/60372/HTML/default/viewer.htm#etsug_arima_sect017....
Assuming you have at least 6 months of data (since proc ARIMA needs at least 6 observations to perform identification step), you may use the following code:
data test;
input date monyy7. ID$ X Y;
format date monyy7.;
datalines;
JAN2018 A1 10 4
JAN2018 B1 27 5
JAN2018 C1 35 22
JAN2018 D1 34 27
JAN2018 F1 24 27
FEB2018 A1 2 44
FEB2018 B1 33 20
FEB2018 C1 0 7
FEB2018 D1 24 44
FEB2018 F1 0 34
MAR2018 A1 42 24
MAR2018 B1 67 2
MAR2018 C1 6 33
MAR2018 D1 0 0
MAR2018 F1 63 24
APR2018 A1 35 24
APR2018 B1 22 2
APR2018 C1 14 33
APR2018 D1 20 0
APR2018 F1 13 24
MAY2018 A1 21 24
MAY2018 B1 70 2
MAY2018 C1 60 33
MAY2018 D1 55 0
MAY2018 F1 55 24
JUN2018 A1 41 24
JUN2018 B1 64 2
JUN2018 C1 60 33
JUN2018 D1 10 0
JUN2018 F1 33 24
;
proc sort data = test out = test;
by ID;
run;
proc arima data = test plots = none out = outX;
by ID;
identify var = X;
estimate p = (1 2 3) ar = (0.3333 0.3333 0.3333) noint noest nostable method = CLS;
forecast lead = 24;
run;
quit;
proc arima data = test plots = none out = outY;
by ID;
identify var = Y;
estimate p = (1 2 3) ar = (0.3333 0.3333 0.3333) noint noest nostable method = CLS;
forecast lead = 24;
run;
quit;
