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 regression-linear 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 space-not necessarily Gaussian-not 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