Seasonal Hidden Markov Models for Stochastic Time Series with Periodically Varying Characteristics
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. Inste...
Main Author: | Lewis, Arthur M. |
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Format: | Others |
Published: |
PDXScholar
1995
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Subjects: | |
Online Access: | https://pdxscholar.library.pdx.edu/open_access_etds/5056 https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=6128&context=open_access_etds |
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