Thank you for your question! I must reflect that this is a profound question as it is never treated in either texts or the classroom.
There is an R version of AUTOBOX (a commercial piece of software I helped to write) which identifies both the stochastic components (ARIMA & possible variance stabilizing transforms) while sorting out/identifying possible deterministic components. This is accomplished by evaluating alternative threads or approaches. The deterministic components are found primarily through proprietary scripted search procedures. For example a particular day-of-the month or week-of-the-month may be important or a change in daily-effects over time which would be detected by evaluating alternative hypothesis/data structures. Pulses,level shifts,local time trends,seasonal pulses are also discovered/detected by search procedures. Long-weekend effects, days-before and after each holiday can also be identified in a similar manner.
The interaction between ARIMA and possible deterministic structure is an added dimension/complication/opportunity that needs to be woven into a self-checking iterative procedure to ensure parsimony and estimability. All of the above needs to be done while accounting for the timely response and anticipation of user specified variables.
I am unaware of any state-space modelling that conducts this level of signal /noise deconstruction . If you wanted to post a real world data set I would be glad to demonstrate the results of AUTOBOX to routinely/automatically perform this task of forming a minimally sufficient model containing both stochastic and deterministic components.