izpis_h1_title_alt

Skriti markovski modeli v analizi finančnih časovnih vrst : delo diplomskega seminarja
ID Praček, Martin (Author), ID Škulj, Damjan (Mentor) More about this mentor... This link opens in a new window

.pdfPDF - Presentation file, Download (229,61 KB)
MD5: 57BC13CA00DDC966BF070036ACD50446
.rR - Appendix, Download (1,94 KB)
MD5: FC6532F4400D9C1E96FA1FB134B12938

Abstract
V moji diplomski nalogi sem se ukvarjal s skritimi markovskimi modeli. Gre za vrsto markovskega modela, kjer ne poznamo stanj, v katerih se model nahaja. Opazujemo lahko le signale, ki o sistemu podajo le posredne informacije. Skozi celotno nalogo predstavim skrite markovske modele, od njihove zgodovine do uporabe v biologiji. Poseben del je posvečen skritim markovskim modelom, ki jih opišemo z Gaussovimi mešanicami. Za te predstavim uporabo Baum-Welchovega in Viterbijevega algoritma. Posvetil sem se tudi časovnim vrstam in njihovim lastnostim. Posebej predstavim finančne časovne vrste in prikažem primer uporabe na le teh. Obenem pa sem opisal še praktični primer, kjer pokažem kako izračunamo prehodno matriko.

Language:Slovenian
Keywords:skriti markovski modeli, časovne vrste, slučajni proces, Gaussova mešanica
Work type:Final seminar paper
Typology:2.11 - Undergraduate Thesis
Organization:FMF - Faculty of Mathematics and Physics
Year:2019
PID:20.500.12556/RUL-110597 This link opens in a new window
UDC:519.2
COBISS.SI-ID:18724697 This link opens in a new window
Publication date in RUL:18.09.2019
Views:2052
Downloads:320
Metadata:XML RDF-CHPDL DC-XML DC-RDF
:
Copy citation
Share:Bookmark and Share

Secondary language

Language:English
Title:Hidden Markov Models in Financial Time Series Analysis
Abstract:
For my graduate thesis I researched Hidden Markov Models, a type of Markov Models where states of model are not known. We can only observe signals, that only show indirect information about the system. Through the paper, it presents Hidden Markov Models from their history, to their use in biology. A part of paper is dedicated to Hidden Markov Models described with Gaussian mixtures. For this models, use of Baum-Welch and Viterbi algorithm is shown. There is also a part about time series and their properties. Financial time series are discussed separately and there is an example of application. Also, I included my own example, where I show the method for calculating tranistion matrix.

Keywords:Hidden Markov Models, Time Series, Stohastic Process, Gaussian mixture

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back