I used to think my time-serie was seasonal, but then I realized it simply needed some calendar-effect adjustment. I tried that and I'm now in doubt there might still be some seasonality left. I tried adding a 12 month differencing, and it seemed to work, but then I thought: why not just try 12 month differencing in the first place? Shouldn't this account for calendar-effects as well, all in one "bundle"?
There you go, runplot + monthly subseries plot + ACF plot of
- raw (top),
- adjusted (top middle),
- 12mths differenced (bottom middle) and
- adjusted+differenced (bottom)
data.
NOTE: the order of the images does not follow the order of my reasoning, I put seasonal differencing before calendar adjustment + seasonal differencing:
How do I know which method gave the correct results (if any)? Is this "science" or just "messing with numbers"?
Would some further cross-validation help (e.g. stl decomposition, seasonal dummies)?
Thank you, as always, for whatever help you can grant me!