Publication:
Smoothness priors in time series

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Authors
Gersch, Will
Kitagawa, Genshiro
Subjects
Bayesian model
smoothness priors
non Gaussian time series
stationary time series
nonstationery time series
Advisors
Date of Issue
1987-04
Date
April 1987
Publisher
Monterey, California. Naval Postgraduate School
Language
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.
Type
Technical Report
Description
Series/Report No
Department
Operations Research (OR)
Other Units
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
NPS55-87-004
Sponsors
Funder
Format
60 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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