STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL

STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL

Statistical dependence exploration of climate data using copula, Markov mo- del, and hidden Markov model will be discussed in this thesis . Many researches on climatology usually use assumptions on the variables being normally distri- buted or having the same marginal distribution function. In re...

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Main Author: Novianti Nurfajri, Rizki
Format: Theses
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/44474
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:44474
spelling id-itb.:444742019-10-22T13:43:25ZSTATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL Novianti Nurfajri, Rizki Indonesia Theses Copula, hidden Markov model INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/44474 Statistical dependence exploration of climate data using copula, Markov mo- del, and hidden Markov model will be discussed in this thesis . Many researches on climatology usually use assumptions on the variables being normally distri- buted or having the same marginal distribution function. In reality, only some variables are normally distributed. In addition, each variable would not have same marginal distribution function. These assumptions cause the analysis result to be far from reality. As an alternative the problem can be solved by copula method. In this thesis, copula method will be applied to study the dependence to sequence time series data. Transition probability of one state to other state in Markov model will then be determined and used to estimate next state using hidden Markov model. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Statistical dependence exploration of climate data using copula, Markov mo- del, and hidden Markov model will be discussed in this thesis . Many researches on climatology usually use assumptions on the variables being normally distri- buted or having the same marginal distribution function. In reality, only some variables are normally distributed. In addition, each variable would not have same marginal distribution function. These assumptions cause the analysis result to be far from reality. As an alternative the problem can be solved by copula method. In this thesis, copula method will be applied to study the dependence to sequence time series data. Transition probability of one state to other state in Markov model will then be determined and used to estimate next state using hidden Markov model.
format Theses
author Novianti Nurfajri, Rizki
spellingShingle Novianti Nurfajri, Rizki
STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL
author_facet Novianti Nurfajri, Rizki
author_sort Novianti Nurfajri, Rizki
title STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL
title_short STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL
title_full STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL
title_fullStr STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL
title_full_unstemmed STATISTICAL DEPENDENCE EXPLORATION OF CLIMATE DATA USING COPULA, MARKOV MODEL, AND HIDDEN MARKOV MODEL
title_sort statistical dependence exploration of climate data using copula, markov model, and hidden markov model
url https://digilib.itb.ac.id/gdl/view/44474
_version_ 1822270683452801024

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