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Activity Number: 132 - Functional Data and Time Series
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #307199 Presentation
Title: Functional Autoregressive Model Using Signal Compression
Author(s): Husneara Rahman* and Xin Qi
Companies: Georgia State University and Georgia State University
Keywords: Functional time series; functiona autoregressive model; signal compression; window-shifting cross-validation

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.

Authors who are presenting talks have a * after their name.

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