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Seasonal Hidden Markov Models for Stochastic Time Series with Periodically Varying Characteristics

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Τίτλος: Seasonal Hidden Markov Models for Stochastic Time Series with Periodically Varying Characteristics
Συγγραφείς: Lewis, Arthur M.
Πηγή: Dissertations and Theses
Στοιχεία εκδότη: PDXScholar
Έτος έκδοσης: 1995
Συλλογή: Portland State University: PDXScholar
Θεματικοί όροι: Markov processes -- Computer programs, Precipitation variability -- Computer programs, Electrical and Computer Engineering, Electrical and Electronics
Περιγραφή: 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.
Τύπος εγγράφου: text
Περιγραφή αρχείου: application/pdf
Γλώσσα: English
Relation: https://pdxscholar.library.pdx.edu/open_access_etds/5056; https://pdxscholar.library.pdx.edu/context/open_access_etds/article/6128/viewcontent/Lewis_Arthur_1995.pdf
DOI: 10.15760/etd.6932
Διαθεσιμότητα: https://pdxscholar.library.pdx.edu/open_access_etds/5056
https://doi.org/10.15760/etd.6932
https://pdxscholar.library.pdx.edu/context/open_access_etds/article/6128/viewcontent/Lewis_Arthur_1995.pdf
Rights: In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Αριθμός Καταχώρησης: edsbas.54849716
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Items – Name: Title
  Label: Title
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  Data: Seasonal Hidden Markov Models for Stochastic Time Series with Periodically Varying Characteristics
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Lewis%2C+Arthur+M%2E%22">Lewis, Arthur M.</searchLink>
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  Data: Dissertations and Theses
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  Data: PDXScholar
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 1995
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  Data: Portland State University: PDXScholar
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  Data: <searchLink fieldCode="DE" term="%22Markov+processes+--+Computer+programs%22">Markov processes -- Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Precipitation+variability+--+Computer+programs%22">Precipitation variability -- Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+and+Computer+Engineering%22">Electrical and Computer Engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Electrical+and+Electronics%22">Electrical and Electronics</searchLink>
– Name: Abstract
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  Group: Ab
  Data: 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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  Data: 10.15760/etd.6932
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  Label: Rights
  Group: Cpyrght
  Data: In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
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      – SubjectFull: Precipitation variability -- Computer programs
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