First Advisor

Andrew M. Fraser

Date of Publication

7-5-1995

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Electrical Engineering

Department

Electrical Engineering

Language

English

Subjects

Markov processes -- Computer programs, Precipitation variability -- Computer programs

DOI

10.15760/etd.6932

Physical Description

1 online resource (v, 100 p.)

Abstract

Novel seasonal hidden Markov models (SHMMs) for stochastic time series with periodically varying characteristics are developed. Nonlinear interactions among SHMM parameters prevent the use of the forward-backward algorithms which are usually used to fit hidden Markov models to a data sequence. Instead, Powell's direction set method for optimizing a function is repeatedly applied to adjust SHMM parameters to fit a data sequence. SHMMs are applied to a set of meteorological data consisting of 9 years of daily rain gauge readings from four sites. The fitted models capture both the annual patterns and the short term persistence of rainfall patterns across the four sites.

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Comments

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Persistent Identifier

https://archives.pdx.edu/ds/psu/29137

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