Markova teorijas pieeja neparametrisku novērtējumu autoregresijā
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Latvijas Universitāte
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Abstract
Maģistra darba tēma ir Markova teorijas pieeja neparametrisku novērtējumu autoregresijā. Izmantojot Markova īpašību tiek iegūts laikrindas prognozes modelis.
Maģistra darbs ir strukturēts 8 nodaļās, kurās ir apskatīts pētāmo datu raksturs un veikta to analīze, veikts teorētiskais izklāsts, izvirzīta problēma laikrindas prognozēšanā un lietota Markova teorijas pieeja prognozes funkcijas atrašanai.
Diplomdarba apjoms ir 41 lappuses. Darbā ir 18 attēli, 17 tabulas. Izmantotās literatūras saraksts sastāv no 8 literatūras avotiem.
The master paper discusses Markov theory approach to nonparametrical autoregressive estimation. Utilizing Markov property there is achieved a model of time-series estimation. The master paper consists of 8 chapters that describe analyses of data and its nature, examined theoretical layout, posed problem for time-series estimation and used Markov theory approach in discovering of estimation function. The scope of the master paper is 41 pages. Also included are 18 graphs, 17 tables. The bibliography lists 8 sources.
The master paper discusses Markov theory approach to nonparametrical autoregressive estimation. Utilizing Markov property there is achieved a model of time-series estimation. The master paper consists of 8 chapters that describe analyses of data and its nature, examined theoretical layout, posed problem for time-series estimation and used Markov theory approach in discovering of estimation function. The scope of the master paper is 41 pages. Also included are 18 graphs, 17 tables. The bibliography lists 8 sources.