This contribution discusses the estimation of an invertible functional time series through fitting of functional moving average processes. The method uses a functional version of the innovations algorithm and dimension reduction onto a number of principal directions. Several methods are suggested to automate the procedures. Empirical evidence is presented in the form of simulations and an application to traffic data.
Order Selection, Functional Principal Component Analysis, Functional Principal Component, Principal Subspace, Innovation Algorithm