I have univariate time series data that I am fitting a gamm to. I am using 10-fold cross validation to choose the best temporal correlation structure in the data. The lowest RMSE value tends to come from an overly complicated ARMA structure (e.g.,(3,2)). I believe the reason may be that with lower order ARMA structures the day covariate smoother becomes overly complex (I think perhaps it is "soaking" in some of the correlation, for lack of a better term). I know a-priori that there is little variability in the data across days, so I think it may help to either fix the k value low (I am thinking 3), or set the max k low for the day covariate. I am not sure how to pick the specific k value, or if this sounds reasonable. Any help is much appreciated, thank-you!
The code I used for all models is shown below, the only change I made was varying the correlation structure for values of p=0-3, and q=0-3.
gam=gamm(sv~s(day,bs="tp")+s(range,bs="tp")+s(time,bs="cc"),data=data.all[1:336,],correlation=corARMA(p=3,q=2),gamma=1.4,control=list(maxIter = 10000, msMaxIter=10000,niterEM=0,msMaxEval=10000))
2 example results from the GAMMs are shown below, note: "day" is a Julian day count for a single month, "range" is the tidal range values, and "time" is a variable with values 1-24 for time of day. The results for the gamm with an AR-1 structure is shown below: