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: | |
|---|---|
| Format: | Others |
| Published: |
PDXScholar
1995
|
| 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 |
| Summary: | 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. |
|---|