Coming from basically no time series back ground, this is likely a simple question, but what is the relationship between "being able to" use an additive decomposition of a series into seasonal, trend and remainder and a Box Cox transformation?
From Professor Hyndman's blog:
Because not all data could be decomposed additively, we first needed to apply an automated Box-Cox transformation.
I was wondering:
1) What makes an additive decomposition attractive relative to a multiplicative one (which I understand is basically the other choice).
2) What is the requirement for an additive decomp and what does Box Cox do to make this possible? I think of Box Cox for ANOVA and reducing heteroskedasticity. Is there a tie in with decomposition of a series?