
Accession Number : ADA180892
Title : Smoothness Priors in Time Series.
Descriptive Note : Technical rept.,
Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA
Personal Author(s) : Gersch,Will ; Kitagawa,Genshiro
Report Date : APR 1987
Pagination or Media Count : 57
Abstract : A variety of time series signal extraction/smoothing problems are considered from a Bayesian 'smoothness priors' point of view. The origin of the subject is a smoothing problem posed by Whittaker (1923). Using a stochastic regressionlinear model Gaussian disturbances framework, we model stationary time series and nonstationary mean and nonstationary covariance time series. Smoothness priors distributions on the model parameters are expressed either in terms of time domain stochastic difference equation or frequency domain constraints. A small number of (hyper)parameters specify very complex time series behavior. The critical computation is the likelihood of the Bayesian model. Finally we show a smoothness priors state spacenot necessarily Gaussiannot necessarily linear model of nonstationary time series. (Author)
Descriptors : *TIME SERIES ANALYSIS, COVARIANCE, SIGNALS, BAYES THEOREM, COMPUTATIONS, EXTRACTION, FREQUENCY, LINEAR SYSTEMS, MATHEMATICAL MODELS, MODELS, STATIONARY
Subject Categories : Numerical Mathematics
Distribution Statement : APPROVED FOR PUBLIC RELEASE