Hello,   I am looking to model data that appears to follow the exponential distribution with a mean of 1.8, as indicated by the screenshots from the CAPABILITY procedure. Exponential Distribution PDF fitting Exponential Distribution CDF fitting Knowing this, how would one go on about trying to build a predictive model for this data? I narrowed down the number of variables to use with the Rapid Predictive Modeler (I do not have Enterprise Miner installed on my computer) and I have tried using the MODEL procedure to do a non-linear modeling of the data but the fit is very poor with an r-square of 0.0674 and a root MSE of 1.8 (to give perspective, the mean of the data is 1.8). Any advice on this topic would be greatly appreciated! PROC MODEL DATA=stdize_data; PARAMETERS w_1 0 w_2 0 w_3 0 w_4 0 lambda 1; BOUNDS w_1 >= 0, w_2 >= 0, w_3 >= 0, w_4 >= 0, lambda > 0; RESTRICT w_1 + w_2 + w_3 + w_4 = 1; CER = lambda * exp(-lambda * (w_1*x_1 + w_2*x_2 + w_3*x_3 + w_4*x_4)); FIT CER / MAXITER=10000 OUT=model_results OUTALL; RUN; ... View more

The data I am working with consists of airline revenue, passenger, and airfare by week and split by four travel regions, dating back to year 2011. The goal for me is to produce a weekly forecast of the three metrics mentioned before. I need to be able to account for when a holiday is occurring for a particular week and what I learned from SAS' support team is that both ARIMA and UCM can be used to account for such events so I decided to try out ARIMA. To account for the holiday effect I set up some variables for each holiday of interest (Christmas, Thanksgiving, Independence Day, etc.) that have 1's to indicate if the holiday occurred or will occur on that week. Then in PROC ARIMA I use the crosscorr option in the identify statement and the input option in the estimate statement to list the holiday variables. The results are not working as well as I expected as some of the predictions for some of the holidays don't make as much of an adjustment as I would expect. Am I going about this the correct way? Are there other ways of tackling this problem? proc arima data=fare_data plots=all out=fare_fcast; identify var=log_fare(1,52) crosscorr=(christmas independence_day thanksgiving memorial_day labor_day new_year) stationarity=(adf=(1,2)); estimate q=(1)(52) input=(christmas independence_day thanksgiving memorial_day labor_day new_year) method=ml outest=estimates; outlier id=wk_end_dt; forecast lead=57 id=wk_end_dt interval=week align=middle printall; run; ... View more