I have settled on building a generalized additive mixed model using
mgcv::gamm, on data and for purposes I have described in more detail here. In a nutshell, I want to explain variations in monthly tourist numbers at two historic sites, depending on predictors such as weather and economic factors (e.g., Consumer Confidence Index), etc. All this, while taking into account the seasonal pattern in visitor numbers, an increasing trend in visitors over the years, and any autoregressive process in the data. Hopefully the choice of
gamm() for modelling this scenario is reasonable. (Also, I am not really concerned with forecasts, rather just a good explanatory model.)
After checking out various sources (e.g., Gavin Simpson's blog posts here and here), there seems to be no mention of assessing stationarity before running such a generalized additive mixed model - yet, this appears to be a major point of focus with time series, generally. I am not clear why this is, and whether me just running
gamm() directly on my data is fine (with no differencing done beforehand etc). I am assuming yes, but would rather make sure. Thanks!