Enter the forecast with underlying factors. If I add Risk Score as my variable to forecast and drag in most of the measures available in my data set, I only see Claim Count, Gross Losses, and Time in Force (number of years as a customer) as my underlying factors that have an influence on risk score.
Now, keep in mind that these can change depending on adding or deleting the underlying factors. The moral of the story is that we have a clear example that correlation and forecast results do not necessarily have to match because correlation does not imply causation.
Just because my Risk Score and Travel Time to work variables are highly correlated, does not mean that Travel Time to work causes a high risk score. As intuitive as it may seem, the underlying factors are based on statistical significance, not on what makes sense from a business point of view. Understanding, even at just a high level, the inner workings of forecasts helps me reconcile this in my head and feel confident that I’m providing others with accurate results. And to me, that’s very comforting.
If you’re interested in learning more about SAS Visual Analytics or SAS Visual Statistics, what better way to do so then by trying it out for yourself? Don’t forget to let us know what you think!