SEASONAL ARIMA

[MANGALA]

I want to forecast for seasonal arima for weekly data. so with this regard, should i consider 1,12 or 1,52. and wat is the method of interpretation

proc arima data= Banana;

identify var= price(1,12) nlag=15;

run;

proc arima data= Banana;

identify var= price(1,12) nlag=15;

estimate p=(1)(12) q=(1)(12);

forecast lead=5 interval=month out=sar111;

run;

proc arima data= Banana;

identify var= price(1,52) nlag=15;

run;

proc arima data= Banana;

identify var= price(1,52) nlag=15;

estimate p=(1)(52) q=(1)(52);

forecast lead=5 interval=month out=sar111;

run;

[SnurreJensen · Accepted solution]

I would recommend starting with plotting your timeseries - both the original series, but also the (seasonally) differenced series and the seasonal cycles. This will give you an indication of the patterns in your data. Plotting can be done using PROC TIMESERIES which is also part of SAS/ETS. For more info, please have a look at the documentation: The TIMESERIES Procedure :: SAS/ETS(R) 14.1 User's Guide

Once that is done, start using the identify statement in PROC ARIMA to examine which orders of differencing you need - if any, type of lag-structure etc. Once you have that in place you can start producing forecasts. For more information on PROC ARIMA please consult the documentation which can be found here: The TIMESERIES Procedure :: SAS/ETS(R) 14.1 User's Guide

Thanks,

Snurre

[alexchien]

Seasonal differencing usually works better than seasonal AR/MA. Please refer to the following link for more info. 

https://onlinecourses.science.psu.edu/stat510/node/67