Title :
Spectral analysis of segmented data
Author :
de Waele, S. ; Broersen, P.M.T.
Author_Institution :
Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands
Abstract :
Time series analysis is reformulated to allow processing of segmented data. This involves the reformulation of parameter estimation and order selection. Parameter estimation for autoregressive (AR) models is done by fitting a single model to all segments simultaneously. Parameter estimation for moving average (MA) and the combined ARMA models can be derived entirely from long autoregressive models. The finite sample theory required for order selection of AR models has been generalized to segments of data. The resulting algorithm can also deal effectively with segments of unequal length
Keywords :
autoregressive moving average processes; parameter estimation; spectral analysis; time series; ARMA models; autoregressive models; finite sample theory; moving average models; order selection; segmented data; time series analysis; Fluid dynamics; Iterative algorithms; Parameter estimation; Physics; Radar; Reflection; Signal analysis; Spectral analysis; Stochastic processes; Time series analysis;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2000.912756
