132 – Functional Data and Time Series
Functional Autoregressive Model Using Signal Compression
Xin Qi
Georgia State University
Husneara Rahman
Georgia State University
Modern advancement of technology permits us to accumulate more complicated data than before. Unlike the traditional time series where only a scalar or a vector is observed at each time point, in functional time series, a curve is observed at each time point. Correlation exists among the curves observed at different time points. In this paper, we consider a functional autoregressive (FAR) model with general order which is a generalization of the traditional AR model. To fit the FAR model and obtain the estimate of coefficient functions, we propose a signal compression procedure. To determine the optimal tuning parameters and optimal order of FAR model, we propose a window-shifting cross-validation procedure. We compare our proposed procedure to recently developed one using both simulated data and real data, which illustrate the good predictive performance of our method.