I have a large number of datasets where the abscissa is date/time and the ordinate is some response variable. I want to show variation over time as a smooth curve. I generally adjust the choice of fit type and the fit parameters (e.g. lambda) interactively until I get a smoothed curve that subjectively seems informative or a good fit to me.
But it's wholly subjective. Have statisticians thought through the question of whether there are objective ways of determining an optimum degree of smoothing?
I imagine optimum could be defined in a myriad of different ways, so maybe this isnt so easy to answer. If there is literature on this, I'd be grateful for some pointers.
