I have a data set of time series observations with a seasonal trend.
I have de-trended and de-seasonalized it using by first differencing and also taking the log of observations. However, when I want to fit an ARIMA model, It is almost impossible to have a proper fit.
This is my code and I have attached the data set.
proc import datafile="liquor.csv"
out=liquor
dbms=csv
replace;
getnames=no;
run;
data liquor (rename=(var1=sales));
set liquor;
t = _n_ ;
*The log transformation is predeferencing transformation to make constant seasonal variation;
LogSales=log(sales);
*These are three different differencing transformation to make the data stationary;
SALES1=logsales-lag(logsales);
SALES2=logsales-lag12(logsales);
SALES3=logsales-lag(logsales)-lag12(logsales)-lag13(logsales);
x=sales1-lag12(sales1);
run;
proc timeseries data=liquor
out=series
outtrend=trend
outseason=season print=seasons;
id date interval=month accumulate=avg;
var x;
run;
proc arima data=LIQUOR ;
identify var=x esacf ;
estimate p=2 q=3;
run;
Any suggestions and help is greatly appreciated.
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I am closing this discussion due to inactivity.
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