Portland State University. Department of Electrical Engineering.
Andrew M. Fraser
Date of Award
Master of Science (M.S.) in Electrical Engineering
1 online resource (v, 100 p.)
Markov processes -- Computer programs, Precipitation variability -- Computer programs
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.
Lewis, Arthur M., "Seasonal Hidden Markov Models for Stochastic Time Series with Periodically Varying Characteristics" (1995). Dissertations and Theses. Paper 5056.