Events vs. Seasonality variation in Forecast Studio

[bluesky]

I have month-wise data that I'm using to forecast in Forecast Studio.

If I set up events (Pulse) for each month (of historical data) and apply the 'Use if Significant' filter, I'm getting substantially different results compared to not using events and just using the usual seasonality parameters. 

Basically, then it looks like the events are picking up additional variation compared to what the seasonality alone picks up. 

I'm wondering if anyone would have any insights to why this may be happening? Any thoughts greatly appreciated!

Thanks.

[mitrov · Accepted solution]

Seasonal components in time series models are not equivalent to dummy variables for the months, as they are often in linear regression models. Therefore, it is not suprising that you are getting different results.

For example, the following page details the equations for the additive Holt-Winters model, which is an exponential smoothing model with an additive seasonal component. 

https://support.sas.com/documentation/cdl/en/etsug/68148/HTML/default/viewer.htm#etsug_tffordet_sect...

As you can see, the seasonal component S_t is dynamic, in that it depends on the past values S_(t-p), where p is the period. 

In a regression model with seasonal dummies, the seasonal effect is a static additive component which does not vary with time.