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Journal articles on the topic 'Markov chain model'

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Author: Grafiati

Published: 4 June 2021

Last updated: 28 January 2023

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1

Rai, Prerna, and Arvind Lal. "Google PageRank Algorithm: Markov Chain Model and Hidden Markov Model." International Journal of Computer Applications 138, no. 9 (March 17, 2016): 9–13. http://dx.doi.org/10.5120/ijca2016908942.

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2

Hlynka, Myron, and Tolulope Sajobi. "A Markov Chain Fibonacci Model." Missouri Journal of Mathematical Sciences 20, no. 3 (October 2008): 186–99. http://dx.doi.org/10.35834/mjms/1316032778.

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3

Berchtold, Andre. "The double chain markov model." Communications in Statistics - Theory and Methods 28, no. 11 (January 1999): 2569–89. http://dx.doi.org/10.1080/03610929908832439.

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4

Yang, Chuan-sheng, Yu-jia Zheng, and Chao Wang. "Incremental multivariate Markov chain model." Journal of Engineering 2018, no. 16 (November 1, 2018): 1433–35. http://dx.doi.org/10.1049/joe.2018.8278.

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5

Boys, R. J., and D. A. Henderson. "On Determining the Order of Markov Dependence of an Observed Process Governed by a Hidden Markov Model." Scientific Programming 10, no. 3 (2002): 241–51. http://dx.doi.org/10.1155/2002/683164.

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This paper describes a Bayesian approach to determining the order of a finite state Markov chain whose transition probabilities are themselves governed by a homogeneous finite state Markov chain. It extends previous work on homogeneous Markov chains to more general and applicable hidden Markov models. The method we describe uses a Markov chain Monte Carlo algorithm to obtain samples from the (posterior) distribution for both the order of Markov dependence in the observed sequence and the other governing model parameters. These samples allow coherent inferences to be made straightforwardly in contrast to those which use information criteria. The methods are illustrated by their application to both simulated and real data sets.

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6

Valenzuela, Mississippi. "Markov chains and applications." Selecciones Matemáticas 9, no. 01 (June 30, 2022): 53–78. http://dx.doi.org/10.17268/sel.mat.2022.01.05.

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This work has three important purposes: first it is the study of Markov Chains, the second is to show that Markov chains have different applications and finally it is to model a process of this behaves. Throughout this work we will describe what a Markov chain is, what these processes are for and how these chains are classified. We will describe a Markov Chain, that is, analyze what are the primary elements that make up a Markov chain, among others.

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7

Gerontidis, Ioannis I. "Semi-Markov Replacement Chains." Advances in Applied Probability 26, no. 3 (September 1994): 728–55. http://dx.doi.org/10.2307/1427818.

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We consider an absorbing semi-Markov chain for which each time absorption occurs there is a resetting of the chain according to some initial (replacement) distribution. The new process is a semi-Markov replacement chain and we study its properties in terms of those of the imbedded Markov replacement chain. A time-dependent version of the model is also defined and analysed asymptotically for two types of environmental behaviour, i.e. either convergent or cyclic. The results contribute to the control theory of semi-Markov chains and extend in a natural manner a wide variety of applied probability models. An application to the modelling of populations with semi-Markovian replacements is also presented.

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8

Gerontidis, Ioannis I. "Semi-Markov Replacement Chains." Advances in Applied Probability 26, no. 03 (September 1994): 728–55. http://dx.doi.org/10.1017/s0001867800026525.

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We consider an absorbing semi-Markov chain for which each time absorption occurs there is a resetting of the chain according to some initial (replacement) distribution. The new process is a semi-Markov replacement chain and we study its properties in terms of those of the imbedded Markov replacement chain. A time-dependent version of the model is also defined and analysed asymptotically for two types of environmental behaviour, i.e. either convergent or cyclic. The results contribute to the control theory of semi-Markov chains and extend in a natural manner a wide variety of applied probability models. An application to the modelling of populations with semi-Markovian replacements is also presented.

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9

Kwon, Hyun-Han, and Byung-Sik Kim. "Development of Statistical Downscaling Model Using Nonstationary Markov Chain." Journal of Korea Water Resources Association 42, no. 3 (March 31, 2009): 213–25. http://dx.doi.org/10.3741/jkwra.2009.42.3.213.

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10

Umurzakov, Uktam. "PREDICTION OF PRICES FOR AGRICULTURAL PRODUCTS THROUGH MARKOV CHAIN MODEL." International Journal of Psychosocial Rehabilitation 24, no. 03 (February 18, 2020): 293–303. http://dx.doi.org/10.37200/ijpr/v24i3/pr200782.

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11

Chung, Gunhui, Kyu Bum Sim, Deok Jun Jo, and Eung Seok Kim. "Hourly Precipitation Simulation Characteristic Analysis Using Markov Chain Model." Journal of Korean Society of Hazard Mitigation 16, no. 3 (June 30, 2016): 351–57. http://dx.doi.org/10.9798/kosham.2016.16.3.351.

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12

Wieczorek, Radosław. "Markov chain model of phytoplankton dynamics." International Journal of Applied Mathematics and Computer Science 20, no. 4 (December 1, 2010): 763–71. http://dx.doi.org/10.2478/v10006-010-0058-7.

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Markov chain model of phytoplankton dynamicsA discrete-time stochastic spatial model of plankton dynamics is given. We focus on aggregative behaviour of plankton cells. Our aim is to show the convergence of a microscopic, stochastic model to a macroscopic one, given by an evolution equation. Some numerical simulations are also presented.

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13

D'Amico, Guglielmo, and Riccardo De Blasis. "A multivariate Markov chain stock model." Scandinavian Actuarial Journal 2020, no. 4 (September 5, 2019): 272–91. http://dx.doi.org/10.1080/03461238.2019.1661280.

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14

Dassios, Angelos, and Hongbiao Zhao. "A Markov Chain Model for Contagion." Risks 2, no. 4 (November 5, 2014): 434–55. http://dx.doi.org/10.3390/risks2040434.

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15

Islam, M. N., and C. D. O’shaughnessy. "On the Markov Chain Binomial Model." Applied Mathematics 04, no. 12 (2013): 1726–30. http://dx.doi.org/10.4236/am.2013.412236.

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16

Otranto, Edoardo. "The multi-chain Markov switching model." Journal of Forecasting 24, no. 7 (2005): 523–37. http://dx.doi.org/10.1002/for.965.

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17

Jain, Sudha. "Markov chain model and its application." Computers and Biomedical Research 19, no. 4 (August 1986): 374–78. http://dx.doi.org/10.1016/0010-4809(86)90049-2.

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18

Kulinich, Max, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson. "A Markov chain method for weighting climate model ensembles." Geoscientific Model Development 14, no. 6 (June 11, 2021): 3539–51. http://dx.doi.org/10.5194/gmd-14-3539-2021.

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Abstract. Climate change is typically modeled using sophisticated mathematical models (climate models) of physical processes that range in temporal and spatial scales. Multi-model ensemble means of climate models show better correlation with the observations than any of the models separately. Currently, an open research question is how climate models can be combined to create an ensemble mean in an optimal way. We present a novel stochastic approach based on Markov chains to estimate model weights in order to obtain ensemble means. The method was compared to existing alternatives by measuring its performance on training and validation data, as well as model-as-truth experiments. The Markov chain method showed improved performance over those methods when measured by the root mean squared error in validation and comparable performance in model-as-truth experiments. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods that address the issues of finding optimal model weight for constructing ensemble means.

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19

Jang, Yoonsun, and Allan S. Cohen. "The Impact of Markov Chain Convergence on Estimation of Mixture IRT Model Parameters." Educational and Psychological Measurement 80, no. 5 (January 9, 2020): 975–94. http://dx.doi.org/10.1177/0013164419898228.

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A nonconverged Markov chain can potentially lead to invalid inferences about model parameters. The purpose of this study was to assess the effect of a nonconverged Markov chain on the estimation of parameters for mixture item response theory models using a Markov chain Monte Carlo algorithm. A simulation study was conducted to investigate the accuracy of model parameters estimated with different degree of convergence. Results indicated the accuracy of the estimated model parameters for the mixture item response theory models decreased as the number of iterations of the Markov chain decreased. In particular, increasing the number of burn-in iterations resulted in more accurate estimation of mixture IRT model parameters. In addition, the different methods for monitoring convergence of a Markov chain resulted in different degrees of convergence despite almost identical accuracy of estimation.

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20

Chen, Pai-Lien, and Pranab Sen. "Markov Chain Model Selection by Misclassified Model Probabilities." Communications in Statistics - Theory and Methods 36, no. 1 (2007): 143–53. http://dx.doi.org/10.1080/03610920600966266.

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21

MacDonald, Iain L. "A coarse-grained Markov chain is a hidden Markov model." Physica A: Statistical Mechanics and its Applications 541 (March 2020): 123661. http://dx.doi.org/10.1016/j.physa.2019.123661.

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22

Pereira, A. G. C., F. A. S. Sousa, B. B. Andrade, and Viviane Simioli Medeiros Campos. "Higher order Markov Chain Model for Synthetic Generation of Daily Streamflows." TEMA (São Carlos) 19, no. 3 (December 17, 2018): 449. http://dx.doi.org/10.5540/tema.2018.019.03.449.

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The aim of this study is to get further into the two-state Markov chain model for synthetic generation daily streamflows. The model proposed in Aksoy and Bayazit (2000) and Aksoy (2003) is based on a two Markov chains for determining the state of the stream. The ascension curve of the hydrograph is modeled by a two-parameter Gamma probability distribution function and is assumed that a recession curve of the hydrograph follows an exponentially function. In this work, instead of assuming a pre-defined order for the Markov chains involved in the modelling of streamflows, a BIC test is performed to establish the Markov chain order that best fit on the data. The methodology was applied to data from seven Brazilian sites. The model proposed here was better than that one proposed by Aksoy but for two sites which have the lowest time series and are located in the driest regions.

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23

Huang, Vincent, and James Unwin. "Markov chain models of refugee migration data." IMA Journal of Applied Mathematics 85, no. 6 (September 29, 2020): 892–912. http://dx.doi.org/10.1093/imamat/hxaa032.

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Abstract The application of Markov chains to modelling refugee crises is explored, focusing on local migration of individuals at the level of cities and days. As an explicit example, we apply the Markov chains migration model developed here to United Nations High Commissioner for Refugees data on the Burundi refugee crisis. We compare our method to a state-of-the-art ‘agent-based’ model of Burundi refugee movements, and highlight that Markov chain approaches presented here can improve the match to data while simultaneously being more algorithmically efficient.

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24

Wang, Chao, and Ting-Zhu Huang. "A New Improved Parsimonious Multivariate Markov Chain Model." Journal of Applied Mathematics 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/902972.

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We present a new improved parsimonious multivariate Markov chain model. Moreover, we find a new convergence condition with a new variability to improve the prediction accuracy and minimize the scale of the convergence condition. Numerical experiments illustrate that the new improved parsimonious multivariate Markov chain model with the new convergence condition of the new variability performs better than the improved parsimonious multivariate Markov chain model in prediction.

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25

Gournellos, Theodore. "A theoretical Markov chain model of the long term landform evolution." Zeitschrift für Geomorphologie 41, no. 4 (December 26, 1997): 519–29. http://dx.doi.org/10.1127/zfg/41/1997/519.

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26

Wang, Chao, Ting-Zhu Huang, and Wai-Ki Ching. "A New Multivariate Markov Chain Model for Adding a New Categorical Data Sequence." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/502808.

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We propose a new multivariate Markov chain model for adding a new categorical data sequence. The number of the parameters in the new multivariate Markov chain model is only𝒪(3s) less than𝒪((s+1)2)the number of the parameters in the former multivariate Markov chain model. Numerical experiments demonstrate the benefits of the new multivariate Markov chain model on saving computational resources.

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27

Liu, Wei, and Chun Chen. "Integration of fast fluid dynamics and Markov chain model for predicting transient particle transport in buildings." E3S Web of Conferences 111 (2019): 04030. http://dx.doi.org/10.1051/e3sconf/201911104030.

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Fast simulation tools for the prediction of transient particle transport are critical in designing the air distribution indoors to reduce the exposure to indoor particles and associated health risks. This investigation proposed a combined fast fluid dynamics (FFD) and Markov chain model for fast predicting transient particle transport indoors. The solver for FFD-Markov-chain model was programmed in OpenFOAM, an open-source CFD toolbox. This study used a case from the literature to validate the developed model and found well agreement between the transient particle concentrations predicted by the FFD-Markov-chain model and the experimental data. This investigation further compared the FFD-Markovchain model with the CFD-Eulerian model and CFD-Lagrangian model in terms of accuracy and efficiency. The accuracy of the FFD-Markov-chain model was similar to that of the other two models. For the studied case, the FFD-Markov-chain model was 4.7 times faster than the CFD-Eulerian model, and it was 137.4 times faster than the CFD-Lagrangian model in predicting the steady-state airflow and transient particle transport. Therefore, the FFD-Markov-chain model is able to greatly reduce the computing cost for predicting transient particle transport in indoor environments.

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28

GAWRON, PIOTR, DARIUSZ KURZYK, and ZBIGNIEW PUCHAŁA. "A MODEL FOR QUANTUM QUEUE." International Journal of Quantum Information 11, no. 02 (March 2013): 1350023. http://dx.doi.org/10.1142/s0219749913500238.

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We consider an extension of discrete time Markov chain queueing model to the quantum domain by use of discrete time quantum Markov chain. We introduce methods for numerical analysis of such models. Using these tools we show that quantum model behaves fundamentally different from the classical one.

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29

Dorrestijn, J., D. T. Crommelin, J. A. Biello, and S. J. Böing. "A data-driven multi-cloud model for stochastic parametrization of deep convection." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371, no. 1991 (May 28, 2013): 20120374. http://dx.doi.org/10.1098/rsta.2012.0374.

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Stochastic subgrid models have been proposed to capture the missing variability and correct systematic medium-term errors in general circulation models. In particular, the poor representation of subgrid-scale deep convection is a persistent problem that stochastic parametrizations are attempting to correct. In this paper, we construct such a subgrid model using data derived from large-eddy simulations (LESs) of deep convection. We use a data-driven stochastic parametrization methodology to construct a stochastic model describing a finite number of cloud states. Our model emulates, in a computationally inexpensive manner, the deep convection-resolving LES. Transitions between the cloud states are modelled with Markov chains. By conditioning the Markov chains on large-scale variables, we obtain a conditional Markov chain, which reproduces the time evolution of the cloud fractions. Furthermore, we show that the variability and spatial distribution of cloud types produced by the Markov chains become more faithful to the LES data when local spatial coupling is introduced in the subgrid Markov chains. Such spatially coupled Markov chains are equivalent to stochastic cellular automata.

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30

YAN, Jiong. "Deriving Software Markov Chain Usage Model from UML Models." Journal of Software 16, no. 8 (2005): 1386. http://dx.doi.org/10.1360/jos161386.

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31

Fronk, Eva-Maria, and Paolo Giudici. "Markov Chain Monte Carlo model selection for DAG models." Statistical Methods and Applications 13, no. 3 (December 2004): 259–73. http://dx.doi.org/10.1007/s10260-004-0097-z.

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32

Zhou, Xiu Ping, Cong Zhu, and Tian Xiang Li. "Annual Runoff Prediction Based on Least Square Support Vector Machines-Markov Chain Combined Model." Advanced Materials Research 418-420 (December 2011): 2114–17. http://dx.doi.org/10.4028/www.scientific.net/amr.418-420.2114.

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A new annual runoff prediction model was proposed by combining the least squares support vector machines and markov chains named LSSVM-MC. Firstly, adopt the least squares support vector machines to predict annual runoff as the first step prediction. Then regarding the series of prediction errors as a process of Markov chain, adopt Markov chain method to predict the potential error as the second step prediction. At last, subtract the two-step prediction results to get the final prediction value. The case study shows that the combined model effectively improves the annual runoff prediction accuracy.

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33

Shirgave, Suresh, Prakash Kulkarni, and José Borges. "Semantically Enriched Variable Length Markov Chain Model for Analysis of User Web Navigation Sessions." International Journal of Information Technology & Decision Making 13, no. 04 (July 2014): 721–53. http://dx.doi.org/10.1142/s0219622014500643.

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The rapid growth of the World Wide Web has resulted in intricate Web sites, demanding enhanced user skills to find the required information and more sophisticated tools that are able to generate apt recommendations. Markov Chains have been widely used to generate next-page recommendations; however, accuracy of such models is limited. Herein, we propose the novel Semantic Variable Length Markov Chain Model (SVLMC) that combines the fields of Web Usage Mining and Semantic Web by enriching the Markov transition probability matrix with rich semantic information extracted from Web pages. We show that the method is able to enhance the prediction accuracy relatively to usage-based higher order Markov models and to semantic higher order Markov models based on ontology of concepts. In addition, the proposed model is able to handle the problem of ambiguous predictions. An extensive experimental evaluation was conducted on two real-world data sets and on one partially generated data set. The results show that the proposed model is able to achieve 15–20% better accuracy than the usage-based Markov model, 8–15% better than the semantic ontology Markov model and 7–12% better than semantic-pruned Selective Markov Model. In summary, the SVLMC is the first work proposing the integration of a rich set of detailed semantic information into higher order Web usage Markov models and experimental results reveal that the inclusion of detailed semantic data enhances the prediction ability of Markov models.

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34

Zhang, Huidi, and Yimao Chen. "Analysis and Application of Grey-Markov Chain Model in Tax Forecasting." Journal of Mathematics 2021 (December 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/9918411.

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Tax data is a typical time series data, which is subject to the interaction and influence of economic and political factors and has dynamic and highly nonlinear characteristics. The key to correct tax forecasting is the choice of forecasting algorithm. Traditional tax forecasting methods, such as factor scoring method, factor regression method, and system adjustment method, have a certain guiding role in actual work, but there are still many shortcomings, such as the limitation from the distribution and size of sample data and difficulty of grasping the nonlinear phenomena in economic system. Grey-Markov chain model formed by the combination of grey forecasting and Markov chain forecasting can not only reveal the general developmental trend of time series data, but also predict their state change patterns. Based on the summary and analysis of previous research works, this paper expounds the current research status and significance of tax forecasting, elaborates the development background, current status, and future challenges of the Grey-Markov chain model, introduces the basic principles of grey forecasting model and Markov chain model, constructs the Grey-Markov chain model, analyzes the model’s residual error and posteriori error tests, conducts the analysis of Grey-Markov chain model, performs grey forecasting model construction and its state division, implements the calculation of transition probability matrix and the determination of tax forecasting value, discusses the application of the Grey-Markov chain model in tax forecasting, and finally carries out a simulation experiment and its result analysis. The study results show that, compared with separate grey forecasting, Markov chain forecasting, and other commonly used time series forecasting methods, the Grey-Markov chain model increases the accuracy of tax forecasts by an average of 2.3–13.1%. This indicates that the combinative forecasting of Grey-Markov chain model can make full use of the information provided by time series data for tax analysis and forecasting. It can not only avoid the influence of economic, political, and human subjective factors, but also have simple calculations, higher accuracy, and stronger practicality. The study results of this paper provide a reference for further researches on the analysis and application of Grey-Markov chain model in tax forecasting.

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35

Mahanta, Janardan, Syed Tanjim Hossain, and Imtiaz Reza. "Impact the Temperature of Bangladesh: An Application of Markov Model." Asia Pacific Journal of Energy and Environment 6, no. 2 (December 31, 2019): 83–90. http://dx.doi.org/10.18034/apjee.v6i2.269.

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Markov chain model has been used to analyze the temperature of Bangladesh. Different order Markov chain model has constructed and their significance has been tested. Using Cramer’s , strength the association of temperature with the order of Markov chain has been measured. Stationary probability has been calculated, and there have been employed whether the temperature is stationary or not.

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36

Mahanta, Janardan, Syed Tanjim Hossain, and Imtiaz Reza. "Impact the Temperature of Bangladesh: An Application of Markov Model." Asia Pacific Journal of Energy and Environment 7, no. 1 (January 28, 2020): 7–14. http://dx.doi.org/10.18034/apjee.v7i1.269.

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Markov chain model has been used to analyze the temperature of Bangladesh. Different order Markov chain model has constructed and their significance has been tested. Using Cramer’s , strength the association of temperature with the order of Markov chain has been measured. Stationary probability has been calculated, and there have been employed whether the temperature is stationary or not.

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37

Norberg, Ragnar. "The Markov Chain Market." ASTIN Bulletin 33, no. 02 (November 2003): 265–87. http://dx.doi.org/10.2143/ast.33.2.503693.

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We consider a financial market driven by a continuous time homogeneous Markov chain. Conditions for absence of arbitrage and for completeness are spelled out, non-arbitrage pricing of derivatives is discussed, and details are worked out for some cases. Closed form expressions are obtained for interest rate derivatives. Computations typically amount to solving a set of first order partial differential equations. An excursion into risk minimization in the incomplete case illustrates the matrix techniques that are instrumental in the model.

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38

Norberg, Ragnar. "The Markov Chain Market." ASTIN Bulletin 33, no. 2 (November 2003): 265–87. http://dx.doi.org/10.1017/s0515036100013465.

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We consider a financial market driven by a continuous time homogeneous Markov chain. Conditions for absence of arbitrage and for completeness are spelled out, non-arbitrage pricing of derivatives is discussed, and details are worked out for some cases. Closed form expressions are obtained for interest rate derivatives. Computations typically amount to solving a set of first order partial differential equations. An excursion into risk minimization in the incomplete case illustrates the matrix techniques that are instrumental in the model.

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39

Scapini, Valeria, and Eduardo Zuñiga. "A markov chain approach to model reconstruction." International Journal of Computational Methods and Experimental Measurements 8, no. 4 (November 19, 2020): 316–25. http://dx.doi.org/10.2495/cmem-v8-n4-316-325.

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40

Sproston, J., and S. Donatelli. "Backward Bisimulation in Markov Chain Model Checking." IEEE Transactions on Software Engineering 32, no. 8 (August 2006): 531–46. http://dx.doi.org/10.1109/tse.2006.74.

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41

Danaher, Peter J. "A MARKOV CHAIN MODEL FOR MAGAZINE EXPOSURE." Australian Journal of Statistics 32, no. 2 (June 1990): 163–76. http://dx.doi.org/10.1111/j.1467-842x.1990.tb01010.x.

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42

Voskoglou, M. G., and S. Perdikaris. "A Markov chain model in problem solving." International Journal of Mathematical Education in Science and Technology 22, no. 6 (November 1991): 909–14. http://dx.doi.org/10.1080/0020739910220607.

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43

Drton, Mathias, Caren Marzban, Peter Guttorp, and Joseph T. Schaefer. "A Markov Chain Model of Tornadic Activity." Monthly Weather Review 131, no. 12 (December 2003): 2941–53. http://dx.doi.org/10.1175/1520-0493(2003)131<2941:amcmot>2.0.co;2.

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44

Gansted, Lise, Rune Brincker, and Lars Pilegaard Hansen. "Fracture mechanical Markov chain crack growth model." Engineering Fracture Mechanics 38, no. 6 (January 1991): 475–89. http://dx.doi.org/10.1016/0013-7944(91)90097-k.

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45

Kuusk, Andres. "A Markov chain model of canopy reflectance." Agricultural and Forest Meteorology 76, no. 3-4 (October 1995): 221–36. http://dx.doi.org/10.1016/0168-1923(94)02216-7.

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46

RUDOLFER, STEPHAN M. "A Markov chain model of extrabinomial variation." Biometrika 77, no. 2 (1990): 255–64. http://dx.doi.org/10.1093/biomet/77.2.255.

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47

McMillen, Daniel P., and John F. McDonald. "A Markov Chain model of zoning change." Journal of Urban Economics 30, no. 2 (September 1991): 257–70. http://dx.doi.org/10.1016/0094-1190(91)90040-e.

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48

Georgiev, G. S., V. T. Georgieva, and W. Plieth. "Markov chain model of electrochemical alloy deposition." Electrochimica Acta 51, no. 5 (November 2005): 870–76. http://dx.doi.org/10.1016/j.electacta.2005.05.067.

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49

Georgiev, G. S. "Markov chain model of mixed surfactant systems." Colloid & Polymer Science 274, no. 1 (January 1996): 49–58. http://dx.doi.org/10.1007/bf00658909.

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50

Jatipaningrum, Maria Titah, Kris Suryowati, and Libertania Maria Melania Esti Un. "Prediksi Kurs Rupiah Terhadap Dolar Dengan FTS-Markov Chain Dan Hidden Markov Model." Jurnal Derivat: Jurnal Matematika dan Pendidikan Matematika 6, no. 1 (August 20, 2019): 32–41. http://dx.doi.org/10.31316/j.derivat.v6i1.334.

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Abstract:

Hidden Markov model is a development of the Markov chain where the state cannot be observed directly (hidden), but can only be observed, a set of other observations and combination of fuzzy logic and Markov chain to predict Rupiah exchange rate against the Dollar. The exchange rate of purchasing and exchange rate of saling is divided into four states, namely down large, down small, small rise, and large rise are symbolized respectively S1, S2, S3, and S4. Probability of sequences of observation for 3 days later is computed by forwarding and Backward Algorithm, determine the hidden state sequence using the viterbi algorithm and estimate the HMM parameters using the Baum Welch algorithm. The MAPE result exchange rate of purchase of FTS-Markov Chain is 1,355% and the exchange rate of sale of FTS-Markov Chain is 1,317%. The sequences of observation which optimized within exchange rate of purchase is X* = {S3,S3,S3}, within exchange rate of sale is also X* = {S3,S3,S3}. Keywords: Exchange rate, FTS-Markov Chain, Hidden Markov Model

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