14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper ThB3.3

Broersen, Piet M.T. (Delft Univ. of Tech.)

Continuous-Time Autoregressive Spectral Analysis for Irregularly Sampled Data

Scheduled for presentation during the Regular Session "Identification of Continuous Time Systems" (ThB3), Thursday, March 30, 2006, 16:10−16:30, Hunter Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Time Series, Maximum Likelihood Methods


A continuous-time autoregressive spectral estimator is introduced that applies the principles of a discrete-time automatic equidistant missing data algorithm to unevenly spaced data. This time series estimator approximates the irregular data by a number of equidistantly resampled missing data sets, with a special nearest neighbor method. The ARMAsel-irreg algorithm estimates and automatically selects a discrete-time AR model from a number of candidates. This selected model often has a number of spurious high frequency poles, which are incompatible with the continuous character of the irregularly sampled signal. Those spurious poles can be eliminated, by transforming only the poles of the discrete time model with a positive real part to matching continuous-time poles. The estimated continuous-time spectra can be accurate at frequencies much higher than the mean data rate. Copyright 2006 IFAC.