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Journal articles on the topic 'Hidden state Markov model'

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Published: 4 June 2021

Last updated: 26 January 2023

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1

Tumilaar, Kezia, Yohanes Langi, and Altien Rindengan. "Hidden Markov Model." d'CARTESIAN 4, no. 1 (February 10, 2015): 86. http://dx.doi.org/10.35799/dc.4.1.2015.8104.

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Hidden Markov Models (HMM) is a stochastic model and is essentially an extension of Markov Chain. In Hidden Markov Model (HMM) there are two types states: the observable states and the hidden states. The purpose of this research are to understand how hidden Markov model (HMM) and to understand how the solution of three basic problems on Hidden Markov Model (HMM) which consist of evaluation problem, decoding problem and learning problem. The result of the research is hidden Markov model can be defined as . The evaluation problem or to compute probability of the observation sequence given the model P(O|) can solved by Forward-Backward algorithm, the decoding problem or to choose hidden state sequence which is optimal can solved by Viterbi algorithm and learning problem or to estimate hidden Markov model parameter to maximize P(O|) can solved by Baum – Welch algorithm. From description above Hidden Markov Model with state 3 can describe behavior from the case studies. Key words: Decoding Problem, Evaluation Problem, Hidden Markov Model, Learning Problem

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2

Ye, Fei, and Yifei Wang. "A Novel Method for Decoding Any High-Order Hidden Markov Model." Discrete Dynamics in Nature and Society 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/231704.

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This paper proposes a novel method for decoding any high-order hidden Markov model. First, the high-order hidden Markov model is transformed into an equivalent first-order hidden Markov model by Hadar’s transformation. Next, the optimal state sequence of the equivalent first-order hidden Markov model is recognized by the existing Viterbi algorithm of the first-order hidden Markov model. Finally, the optimal state sequence of the high-order hidden Markov model is inferred from the optimal state sequence of the equivalent first-order hidden Markov model. This method provides a unified algorithm framework for decoding hidden Markov models including the first-order hidden Markov model and any high-order hidden Markov model.

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Suharleni, Farida, Agus Widodo, and Endang Wahyu H. "Hidden Markov Model Application to Transfer The Trader Online Forex Brokers." CAUCHY 2, no. 2 (May 4, 2012): 66. http://dx.doi.org/10.18860/ca.v2i2.2222.

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<p>Hidden Markov Model is elaboration of Markov chain, which is applicable to cases that can’t directly observe. In this research, Hidden Markov Model is used to know trader’s transition to broker forex online. In Hidden Markov Model, observed state is observable part and hidden state is hidden part. Hidden Markov Model allows modeling system that contains interrelated observed state and hidden state. As observed state in trader’s transition to broker forex online is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online, whereas as hidden state is broker forex online Marketiva, Masterforex, Instaforex, FBS and Others. First step on application of Hidden Markov Model in this research is making construction model by making a probability of transition matrix (A) from every broker forex online. Next step is making a probability of observation matrix (B) by making conditional probability of five categories, that is category 1, category 2, category 3, category 4, category 5 by condition of every broker forex online and also need to determine an initial state probability (π) from every broker forex online. The last step is using Viterbi algorithm to find hidden state sequences that is broker forex online sequences which is the most possible based on model and observed state that is the five categories. Application of Hidden Markov Model is done by making program with Viterbi algorithm using Delphi 7.0 software with observed state based on simulation data. Example: By the number of observation T = 5 and observed state sequences O = (2,4,3,5,1) is found hidden state sequences which the most possible with observed state O as following : where X1 = FBS, X2 = Masterforex, X3 = Marketiva, X4 = Others, and X5 = Instaforex.</p>

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Valera, Isabel, Francisco J. R. Ruiz, and Fernando Perez-Cruz. "Infinite Factorial Unbounded-State Hidden Markov Model." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 9 (September 1, 2016): 1816–28. http://dx.doi.org/10.1109/tpami.2015.2498931.

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5

Yanyi, Xu, and Lv Jinhua. "State Recognition Based on Hidden Markov Model." International Journal of Multimedia and Ubiquitous Engineering 11, no. 2 (February 28, 2016): 389–98. http://dx.doi.org/10.14257/ijmue.2016.11.2.38.

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Wang, Bing, Ping Yan, Qiang Zhou, and Libing Feng. "State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 11 (January 27, 2016): 2135–46. http://dx.doi.org/10.1177/0954406215626942.

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Large spot welder is an important equipment in rail transit equipment manufacturing industry, but having the problem of low utilization rate and low effectlvely machining rate. State monitoring can master its operating states real time and comprehensively, and providing data support for state recognition. Hidden Markov model is a state classification method, but it is sensitive to the initial model parameters and easy to trap into a local optima. Genetic algorithm is a global searching method; however, it is quite poor at hill climbing and also has the problem of premature convergence. In this paper, proposing the improved genetic algorithm, and combining improved genetic algorithm and hidden Markov model, a new method of state recognition method named improved genetic algorithm–hidden Markov model is proposed. In the proposed method, improved genetic algorithm is used for optimizing the initial parameters, and hidden Markov model as a classifier to recognize the operating states for machining process. This method is also compared with the other two recognition methods named adaptive genetic algorithm–hidden Markov model and hidden Markov model, in which adaptive genetic algorithm is similarly used for optimizing the initial parameters, however hidden Markov model (in both methods) as a classifier. Experimental results show that the proposed method is very effective, and the improved genetic algorithm–hidden Markov model recognition method is superior to the adaptive genetic algorithm–hidden Markov model and hidden Markov model recognition method.

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Kimball, Steven F., and Joanne Como. "Cascaded hidden Markov model for meta-state estimation." Journal of the Acoustical Society of America 119, no. 4 (2006): 1919. http://dx.doi.org/10.1121/1.2195851.

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8

Goodall, Victoria L., Sam M. Ferreira, Paul J. Funston, and Nkabeng Maruping-Mzileni. "Uncovering hidden states in African lion movement data using hidden Markov models." Wildlife Research 46, no. 4 (2019): 296. http://dx.doi.org/10.1071/wr18004.

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Context Direct observations of animals are the most reliable way to define their behavioural characteristics; however, to obtain these observations is costly and often logistically challenging. GPS tracking allows finer-scale interpretation of animal responses by measuring movement patterns; however, the true behaviour of the animal during the period of observation is seldom known. Aims The aim of our research was to draw behavioural inferences for a lioness with a hidden Markov model and to validate the predicted latent-state sequence with field observations of the lion pride. Methods We used hidden Markov models to model the movement of a lioness in the Kruger National Park, South Africa. A three-state log-normal model was selected as the most suitable model. The model outputs are related to collected data by using an observational model, such as, for example, a distribution for the average movement rate and/or direction of movement that depends on the underlying model states that are taken to represent behavioural states of the animal. These inferred behavioural states are validated against direct observation of the pride’s behaviour. Key results Average movement rate provided a useful alternative for the application of hidden Markov models to irregularly spaced GPS locations. The movement model predicted resting as the dominant activity throughout the day, with a peak in the afternoon. The local-movement state occurred consistently throughout the day, with a decreased proportion during the afternoon, when more resting takes place, and an increase towards the early evening. The relocating state had three peaks, namely, during mid-morning, early evening and about midnight. Because of the differences in timing of the direct observations and the GPS locations, we had to compare point observations of the true behaviour with an interval prediction of the modelled behavioural state. In 75% of the cases, the model-predicted behaviour and the field-observed behaviour overlapped. Conclusions Our data suggest that the hidden Markov modelling approach is successful at predicting a realistic behaviour of lions on the basis of the GPS location coordinates and the average movement rate between locations. The present study provided a unique opportunity to uncover the hidden states and compare the true behaviour with the inferred behaviour from the predicted state sequence. Implications Our results illustrated the potential of using hidden Markov models with movement rate as an input to understand carnivore behavioural patterns that could inform conservation management practices.

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Liao, Yiwei, Guosheng Zhao, Jian Wang, and Shu Li. "Network Security Situation Assessment Model Based on Extended Hidden Markov." Mathematical Problems in Engineering 2020 (August 24, 2020): 1–13. http://dx.doi.org/10.1155/2020/1428056.

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A network security situation assessment system based on the extended hidden Markov model is designed in this paper. Firstly, the standard hidden Markov model is expanded from five-tuple to seven-tuple, and two parameters of network defense efficiency and risk loss vector are added so that the model can describe network security situation more completely. Then, an initial algorithm of state transition matrix was defined, observation vectors were extracted from the fusion of various system security detection data, the network state transition matrix was created and modified by the observation vectors, and a solution procedure of the hidden state probability distribution sequence based on extended hidden Markov model was derived. Finally, a method of calculating risk loss vector according to the international definition was designed and the current network risk value was calculated by the hidden state probability distribution; then the global security situation was assessed. The experiment showed that the model satisfied practical applications and the assessment result is accurate and effective.

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Wang, Ning, Shu-dong Sun, Zhi-qiang Cai, Shuai Zhang, and Can Saygin. "A Hidden Semi-Markov Model with Duration-Dependent State Transition Probabilities for Prognostics." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/632702.

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Realistic prognostic tools are essential for effective condition-based maintenance systems. In this paper, a Duration-Dependent Hidden Semi-Markov Model (DD-HSMM) is proposed, which overcomes the shortcomings of traditional Hidden Markov Models (HMM), including the Hidden Semi-Markov Model (HSMM): (1) it allows explicit modeling of state transition probabilities between the states; (2) it relaxes observations’ independence assumption by accommodating a connection between consecutive observations; and (3) it does not follow the unrealistic Markov chain’s memoryless assumption and therefore it provides a more powerful modeling and analysis capability for real world problems. To facilitate the computation of the proposed DD-HSMM methodology, new forward-backward algorithm is developed. The demonstration and evaluation of the proposed methodology is carried out through a case study. The experimental results show that the DD-HSMM methodology is effective for equipment health monitoring and management.

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ELLIOTT, ROBERT J., and BING HAN. "A HIDDEN MARKOV APPROACH TO THE FORWARD PREMIUM PUZZLE." International Journal of Theoretical and Applied Finance 09, no. 07 (November 2006): 1009–20. http://dx.doi.org/10.1142/s0219024906003949.

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A Hidden Markov Chain (HMC) is applied to study the forward premium puzzle. The weekly quotient of the interest rate differential divided by the log exchange rate change is modeled as a Hidden Markov process. Compared with existing standard approaches, the Hidden Markov approach allows a detailed analysis of the puzzle on a day-to-day basis while taking into full account the presence of noise in the observations. Two and three state models are investigated. A three-state HMC model performs better than two-state models. Application of the three-state model reveals that the above quotient is mostly zero, and hence leads to the rejection of the uncovered interest rate parity hypothesis.

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Lambert, M. F., J. P. Whiting, and A. V. Metcalfe. "A non-parametric hidden Markov model for climate state identification." Hydrology and Earth System Sciences 7, no. 5 (October 31, 2003): 652–67. http://dx.doi.org/10.5194/hess-7-652-2003.

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Abstract. Hidden Markov models (HMMs) can allow for the varying wet and dry cycles in the climate without the need to simulate supplementary climate variables. The fitting of a parametric HMM relies upon assumptions for the state conditional distributions. It is shown that inappropriate assumptions about state conditional distributions can lead to biased estimates of state transition probabilities. An alternative non-parametric model with a hidden state structure that overcomes this problem is described. It is shown that a two-state non-parametric model produces accurate estimates of both transition probabilities and the state conditional distributions. The non-parametric model can be used directly or as a technique for identifying appropriate state conditional distributions to apply when fitting a parametric HMM. The non-parametric model is fitted to data from ten rainfall stations and four streamflow gauging stations at varying distances inland from the Pacific coast of Australia. Evidence for hydrological persistence, though not mathematical persistence, was identified in both rainfall and streamflow records, with the latter showing hidden states with longer sojourn times. Persistence appears to increase with distance from the coast. Keywords: Hidden Markov models, non-parametric, two-state model, climate states, persistence, probability distributions

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Korolkiewicz, Małgorzata Wiktoria. "A Dependent Hidden Markov Model of Credit Quality." International Journal of Stochastic Analysis 2012 (August 13, 2012): 1–13. http://dx.doi.org/10.1155/2012/719237.

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We propose a dependent hidden Markov model of credit quality. We suppose that the "true" credit quality is not observed directly but only through noisy observations given by posted credit ratings. The model is formulated in discrete time with a Markov chain observed in martingale noise, where "noise" terms of the state and observation processes are possibly dependent. The model provides estimates for the state of the Markov chain governing the evolution of the credit rating process and the parameters of the model, where the latter are estimated using the EM algorithm. The dependent dynamics allow for the so-called "rating momentum" discussed in the credit literature and also provide a convenient test of independence between the state and observation dynamics.

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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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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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15

Liu, Tao, Jin Chen, and Guangming Dong. "Identification of bearing faults using linear discriminate analysis and continuous hidden Markov model." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 230, no. 10 (April 23, 2015): 1658–72. http://dx.doi.org/10.1177/0954406215582015.

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It is important to diagnose the bearing fault to prevent the serious accident of equipment. This paper introduces a bearing fault identification scheme based on envelope power spectrum analysis, linear discriminate analysis and continuous hidden Markov model. First the envelope power spectrum features are extracted from amplitude demodulated vibration signals from fault bearings. Then, linear discriminate analysis is employed to reduce the feature dimensions, which are helpful for improving the computing speed and diagnosing accuracy. At last, the new linear discriminate analysis features are input into continuous hidden Markov model to train the models under different conditions, respectively. In bearing fault identification, test data are input into the pretrained continuous hidden Markov models, and the bearing state can be detected by the output of continuous hidden Markov model. To validate the effectiveness of the proposed method, experimental samples of four bearing conditions at different fault sizes and loads are utilized to test the continuous hidden Markov model and back-propagation neural network. The result shows that continuous hidden Markov model and linear discriminate analysis-based method have higher accuracy and efficiency than back-propagation neural network.

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MacKay, David J. C. "Equivalence of Linear Boltzmann Chains and Hidden Markov Models." Neural Computation 8, no. 1 (January 1996): 178–81. http://dx.doi.org/10.1162/neco.1996.8.1.178.

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Several authors have studied the relationship between hidden Markov models and “Boltzmann chains” with a linear or “time-sliced” architecture. Boltzmann chains model sequences of states by defining state-state transition energies instead of probabilities. In this note I demonstrate that under the simple condition that the state sequence has a mandatory end state, the probability distribution assigned by a strictly linear Boltzmann chain is identical to that assigned by a hidden Markov model.

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Kersting, K., L. De Raedt, and T. Raiko. "Logical Hidden Markov Models." Journal of Artificial Intelligence Research 25 (April 19, 2006): 425–56. http://dx.doi.org/10.1613/jair.1675.

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Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters. This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics.

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Roy, Sushmita, Terran Lane, Chris Allen, Anthony D. Aragon, and Margaret Werner-Washburne. "A Hidden-State Markov Model for Cell Population Deconvolution." Journal of Computational Biology 13, no. 10 (December 2006): 1749–74. http://dx.doi.org/10.1089/cmb.2006.13.1749.

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Evans, J. S., and V. Krishnamurthy. "Hidden Markov model state estimation with randomly delayed observations." IEEE Transactions on Signal Processing 47, no. 8 (1999): 2157–66. http://dx.doi.org/10.1109/78.774757.

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Evans, Jamie, and Vikram Krishnamurthy. "Optimal sensor scheduling for hidden Markov model state estimation." International Journal of Control 74, no. 18 (January 2001): 1737–42. http://dx.doi.org/10.1080/00207170110089752.

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Osman, Asmaa A. E., Reda A. El-Khoribi, Mahmoud E. Shoman, and M. A. Wahby Shalaby. "Trajectory learning using posterior hidden Markov model state distribution." Egyptian Informatics Journal 18, no. 3 (November 2017): 171–80. http://dx.doi.org/10.1016/j.eij.2016.12.003.

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Zhang, Wei, Youbing Gao, and Kun Zheng. "Radar working-state identification using the hidden Markov model." Journal of Engineering 2019, no. 21 (November 1, 2019): 7632–35. http://dx.doi.org/10.1049/joe.2019.0649.

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Yao, Yuan, Yi Cao, Jia Zhai, Junxiu Liu, Mengyuan Xiang, and Lu Wang. "Latent state recognition by an enhanced hidden Markov model." Expert Systems with Applications 161 (December 2020): 113722. http://dx.doi.org/10.1016/j.eswa.2020.113722.

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Oflaz, Zarina Nukeshtayeva, Ceylan Yozgatligil, and A. Sevtap Selcuk-Kestel. "AGGREGATE CLAIM ESTIMATION USING BIVARIATE HIDDEN MARKOV MODEL." ASTIN Bulletin 49, no. 1 (November 29, 2018): 189–215. http://dx.doi.org/10.1017/asb.2018.29.

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AbstractIn this paper, we propose an approach for modeling claim dependence, with the assumption that the claim numbers and the aggregate claim amounts are mutually and serially dependent through an underlying hidden state and can be characterized by a hidden finite state Markov chain using bivariate Hidden Markov Model (BHMM). We construct three different BHMMs, namely Poisson–Normal HMM, Poisson–Gamma HMM, and Negative Binomial–Gamma HMM, stemming from the most commonly used distributions in insurance studies. Expectation Maximization algorithm is implemented and for the maximization of the state-dependent part of log-likelihood of BHMMs, the estimates are derived analytically. To illustrate the proposed model, motor third-party liability claims in Istanbul, Turkey, are employed in the frame of Poisson–Normal HMM under a different number of states. In addition, we derive the forecast distribution, calculate state predictions, and determine the most likely sequence of states. The results indicate that the dependence under indirect factors can be captured in terms of different states, namely low, medium, and high states.

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GHAHRAMANI, ZOUBIN. "AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 15, no. 01 (February 2001): 9–42. http://dx.doi.org/10.1142/s0218001401000836.

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We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective makes it possible to consider novel generalizations of hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.

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Du, Yang, Eduard Murani, Siriluck Ponsuksili, and Klaus Wimmers. "biomvRhsmm:Genomic Segmentation with Hidden Semi-Markov Model." BioMed Research International 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/910390.

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High-throughput technologies like tiling array and next-generation sequencing (NGS) generate continuous homogeneous segments or signal peaks in the genome that represent transcripts and transcript variants (transcript mapping and quantification), regions of deletion and amplification (copy number variation), or regions characterized by particular common features like chromatin state or DNA methylation ratio (epigenetic modifications). However, the volume and output of data produced by these technologies present challenges in analysis. Here, a hidden semi-Markov model (HSMM) is implemented and tailored to handle multiple genomic profile, to better facilitate genome annotation by assisting in the detection of transcripts, regulatory regions, and copy number variation by holistic microarray or NGS. With support for various data distributions, instead of limiting itself to one specific application, the proposed hidden semi-Markov model is designed to allow modeling options to accommodate different types of genomic data and to serve as a general segmentation engine. By incorporating genomic positions into the sojourn distribution of HSMM, with optional prior learning using annotation or previous studies, the modeling output is more biologically sensible. The proposed model has been compared with several other state-of-the-art segmentation models through simulation benchmarking, which shows that our efficient implementation achieves comparable or better sensitivity and specificity in genomic segmentation.

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Debus, Thomas J., Pierre E. Dupont, and Robert D. Howe. "Contact State Estimation Using Multiple Model Estimation and Hidden Markov Models." International Journal of Robotics Research 23, no. 4-5 (April 2004): 399–413. http://dx.doi.org/10.1177/0278364904042195.

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Kim, Hee-Young. "Applications of Poisson Hidden Markov models to PM10 concentrations data." Korean Data Analysis Society 24, no. 4 (August 31, 2022): 1203–12. http://dx.doi.org/10.37727/jkdas.2022.24.4.1203.

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This study addresses the problem of monitoring and forecasting of particulate matter(PM) data, focusing, in particular, on high-level , which is known to adversely impact human mortality and morbidity. We use hourly data, collected between November 26, 2018, to December 01, 2018, from 40 stations located in the Seoul metropolitan area of South Korea. We model the number of regions corresponding to “bad” or “very bad” categories of the density, using a hidden Markov model with Poisson state-dependent distribution, Poisson-HMM, since a Poisson-HMM allows for both overdispersion and serial dependence. Model selection, in particular for the number of latent states, is based on Akaike’s Information criterion and Bayesian Information criterion. By AIC and BIC, the Poisson-HMMs are a big improvement on independent mixture of Poissons with the same state, which does not allow for the serial dependence in the observations. We conclude that Poisson-HMM is a good forecasting model.

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Whiting, J. P., M. F. Lambert, and A. V. Metcalfe. "Modelling persistence in annual Australia point rainfall." Hydrology and Earth System Sciences 7, no. 2 (April 30, 2003): 197–211. http://dx.doi.org/10.5194/hess-7-197-2003.

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Abstract. Annual rainfall time series for Sydney from 1859 to 1999 is analysed. Clear evidence of nonstationarity is presented, but substantial evidence for persistence or hidden states is more elusive. A test of the hypothesis that a hidden state Markov model reduces to a mixture distribution is presented. There is strong evidence of a correlation between the annual rainfall and climate indices. Strong evidence of persistence of one of these indices, the Pacific Decadal Oscillation (PDO), is presented together with a demonstration that this is better modelled by fractional differencing than by a hidden state Markov model. It is shown that conditioning the logarithm of rainfall on PDO, the Southern Oscillation index (SOI), and their interaction provides realistic simulation of rainfall that matches observed statistics. Similar simulation models are presented for Brisbane, Melbourne and Perth. Keywords: Hydrological persistence,hidden state Markov models, fractional differencing, PDO, SOI, Australian rainfall

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Aggoun, Lakhdar, and Robert J. Elliott. "Finite-dimensional models for hidden Markov chains." Advances in Applied Probability 27, no. 1 (March 1995): 146–60. http://dx.doi.org/10.2307/1428101.

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A continuous-time, non-linear filtering problem is considered in which both signal and observation processes are Markov chains. New finite-dimensional filters and smoothers are obtained for the state of the signal, for the number of jumps from one state to another, for the occupation time in any state of the signal, and for joint occupation times of the two processes. These estimates are then used in the expectation maximization algorithm to improve the parameters in the model. Consequently, our filters and model are adaptive, or self-tuning.

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Aggoun, Lakhdar, and Robert J. Elliott. "Finite-dimensional models for hidden Markov chains." Advances in Applied Probability 27, no. 01 (March 1995): 146–60. http://dx.doi.org/10.1017/s0001867800046280.

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A continuous-time, non-linear filtering problem is considered in which both signal and observation processes are Markov chains. New finite-dimensional filters and smoothers are obtained for the state of the signal, for the number of jumps from one state to another, for the occupation time in any state of the signal, and for joint occupation times of the two processes. These estimates are then used in the expectation maximization algorithm to improve the parameters in the model. Consequently, our filters and model are adaptive, or self-tuning.

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Dong, Lei, Jianfei Wang, Ming-Lang Tseng, Zhiyong Yang, Benfu Ma, and Ling-Ling Li. "Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model." Symmetry 12, no. 11 (October 22, 2020): 1750. http://dx.doi.org/10.3390/sym12111750.

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This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies.

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Nkemnole, Edesiri Bridget, and Ekene Nwaokoro. "Modelling Customer Relationships as Hidden Markov Chains." Path of Science 6, no. 11 (November 30, 2020): 5011–19. http://dx.doi.org/10.22178/pos.64-9.

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Models in behavioural relationship marketing suggest that relations between the customer and the company change over time as a result of the continuous encounter. Some theoretical models have been put forward concerning relationship marketing, both from the standpoints of consumer behaviour and empirical modelling. In addition to these, this study proposes the hidden Markov model (HMM) as a potential tool for assessing customer relationships. Specifically, the HMM is submitted via the framework of a Markov chain model to classify customers relationship dynamics of a telecommunication service company by using an experimental data set. We develop and estimate an HMM to relate the unobservable relationship states to the observed buying behaviour of the customers giving an appropriate classification of the customers into the relationship states. By merely accounting for the functional and unobserved heterogeneity with a two-state hidden Markov model and taking estimation into account via an optimal estimation method, the empirical results not only demonstrate the value of the proposed model in assessing the dynamics of a customer relationship over time but also gives the optimal marketing-mixed strategies in different customer state.

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Sidorov, S. M. "Hidden Markov Model of Two-Component System with Group Instantly Replenished Time Reserve." INFORMACIONNYE TEHNOLOGII 27, no. 2 (February 12, 2021): 64–71. http://dx.doi.org/10.17587/it.27.64-71.

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Most systems allow the construction of a semi-Markov model. However, during the operation of the system, full information contained in the state encoding is not always available, but it is possible to obtain some signal (information). Tasks arise to assess the consistency of the model with the received data (signals), to refine the model and its parameters. Such parameters can be characteristics of random values characterizing system operation, time reserve value, etc. The theory of hidden Markov models allows solving these problems. In order to move from a semi-Markov model of the system to its hidden Markov model, it is proposed to first the semi-Markov model merge using a stationary phase merging algorithm. In this paper, on the basis of the semi-Markov model with a common phase state space of a two-component system with a group instantly replenished timereserve, we construct a hidden Markov model of a two-component system with a group instantly replenished time reserve. It is used to evaluate the characteristics and predict the states of the system in question based on the received vector of signals. The influence of the time reserve value on the probability of occurrence of the obtained vector of signals is shown.

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Chi, Cheng Ying, and Yan Zhang. "Information Extraction from Chinese Papers Based on Hidden Markov Model." Advanced Materials Research 846-847 (November 2013): 1291–94. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1291.

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Hidden Markov model HMM (1) is one of the important approaches for information extraction. In this paper, a model of the improved first-order hidden Markov HMM (2) is proposed. In the HMM (2), the output probability of the observation is not only dependent on the current state of the model, but also dependent on the previous state of the current state of the model. The algorithm of the ML and the algorithm of the Viterbi are analyzed. At last, experiments show that the HMM (2) is more precise than the HMM (1).

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Vaseghi, S. V. "State duration modelling in hidden Markov models." Signal Processing 41, no. 1 (January 1995): 31–41. http://dx.doi.org/10.1016/0165-1684(94)00088-h.

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Cartella, Francesco, Jan Lemeire, Luca Dimiccoli, and Hichem Sahli. "Hidden Semi-Markov Models for Predictive Maintenance." Mathematical Problems in Engineering 2015 (2015): 1–23. http://dx.doi.org/10.1155/2015/278120.

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Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

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Sebastian, Tunny, Visalakshi Jeyaseelan, Lakshmanan Jeyaseelan, Shalini Anandan, Sebastian George, and Shrikant I. Bangdiwala. "Decoding and modelling of time series count data using Poisson hidden Markov model and Markov ordinal logistic regression models." Statistical Methods in Medical Research 28, no. 5 (April 4, 2018): 1552–63. http://dx.doi.org/10.1177/0962280218766964.

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Hidden Markov models are stochastic models in which the observations are assumed to follow a mixture distribution, but the parameters of the components are governed by a Markov chain which is unobservable. The issues related to the estimation of Poisson-hidden Markov models in which the observations are coming from mixture of Poisson distributions and the parameters of the component Poisson distributions are governed by an m-state Markov chain with an unknown transition probability matrix are explained here. These methods were applied to the data on Vibrio cholerae counts reported every month for 11-year span at Christian Medical College, Vellore, India. Using Viterbi algorithm, the best estimate of the state sequence was obtained and hence the transition probability matrix. The mean passage time between the states were estimated. The 95% confidence interval for the mean passage time was estimated via Monte Carlo simulation. The three hidden states of the estimated Markov chain are labelled as ‘Low’, ‘Moderate’ and ‘High’ with the mean counts of 1.4, 6.6 and 20.2 and the estimated average duration of stay of 3, 3 and 4 months, respectively. Environmental risk factors were studied using Markov ordinal logistic regression analysis. No significant association was found between disease severity levels and climate components.

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Yao, Zhe He, Xin Li, and Zi Chen Chen. "Prediction of Cutting Chatter Based on Hidden Markov Model." Key Engineering Materials 353-358 (September 2007): 2712–15. http://dx.doi.org/10.4028/www.scientific.net/kem.353-358.2712.

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Self-chatter is a serious problem in cutting process. This paper aims to solve the problem by establishing time series model of vibration acceleration signal in cutting process based on Hidden Markov Model (HMM) technology and achieve the purpose of chatter recognition and prediction. Features which can indicate cutting state are extracted from the acceleration signal. HMM parameters are obtained by model training, and the reference models database is built. Then cutting state recognition is performed according to the feature matching level. Simulations and experiments are conducted, and the results show that the proposed method is feasible and it could get high recognition

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NURHASANAH, N., B. SETIAWATY, and F. BUKHARI. "PEMODELAN POISSON HIDDEN MARKOV UNTUK PREDIKSI BANYAKNYA KECELAKAAN DI JALAN TOL JAKARTA-CIKAMPEK." Journal of Mathematics and Its Applications 15, no. 1 (July 1, 2016): 55. http://dx.doi.org/10.29244/jmap.15.1.55-64.

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Model Poisson hidden Markov digunakan untuk memodelkan banyaknya kecelakaan yang terjadi di jalan tol Jakarta-Cikampek pada tahun 2013- 2014. Data banyaknya kecelakaan merupakan barisan observasi yang mengalami overdispersi dan bergantung pada penyebab kecelakaan yang diasumsikan tidak diamati secara langsung dan membentuk rantai Markov. Model Poisson hidden Markov dicirikan oleh parameternya. Pendugaan parameter model dilakukan dengan menggunakan metode Maksimum Likelihood yang perhitungannya menggunakan algoritme Expectation Maximization. Nilai dugaan parameter digunakan untuk membangkitkan barisan penduga kecelakaan. Keakuratan model diukur menggunakan Mean Absolute Percentage Error (MAPE). Menggunakan kriteria AIC diperoleh model Poisson hidden Markov 2 state sebagai model terbaik dengan nilai MAPE 34.0786% untuk prediksi satu waktu yang akan datang.

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Moon, Todd K., and Jacob H. Gunther. "Continuum-State Hidden Markov Models with Dirichlet State Distributions." Journal of Aerospace Information Systems 12, no. 12 (December 2015): 800–824. http://dx.doi.org/10.2514/1.i010260.

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Adamu, Lawal, Saidu Daudu Yakubu, Edith Ndidiamaka Didigwu, Abdullahi Abubakar, Khadeejah James Audu, and Isaac Adaji. "Application of hidden Markov model in yam yield forecasting." Scientia Africana 21, no. 2 (September 8, 2022): 39–52. http://dx.doi.org/10.4314/sa.v21i2.5.

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Providing the government and farmers with reliable and dependable information about crop yields before each growing season begins is the thrust of this research. A four-state stochastic model was formulated using the principle of Markov, each state of the model has three possible observations. The model is designed to make a forecast of yam yield in the next and subsequent growing seasons given the yam yield in the present growing season. The parameters of the model were estimated from the yam yield data of Niger state, Nigeria for the period of sixteen years (2001-2016). After which, the model was trained using Baum-Welch algorithm to attend maximum likelihood. A short time validity test conduct on the model showed good performance. Both the validity test and the future forecast shows prevalence of High yam yield, this attest to the reality on the ground, that Niger State is one of the largest producers of yam in Nigeria. The general performance of the model, showed that it is reliable therefore, the results from the model could serve as a guide to the yam farmers and the government to plan strategies for high yam production in the region.

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Shue, L., S. Dey, B. D. O. Anderson, and F. De Bruyne. "On state-estimation of a two-state hidden Markov model with quantization." IEEE Transactions on Signal Processing 49, no. 1 (2001): 202–8. http://dx.doi.org/10.1109/78.890362.

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Yang, Yi Huai, Li Fang Wang, and Yan Ping Sun. "Hidden Markov Modeling for Nakagami Fading Channel." Advanced Materials Research 989-994 (July 2014): 2576–79. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2576.

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In this paper, we proposed a novel hidden Markov model (HMM) to approximate the characteristic of the Nakagami Fading channel. Our study is based on a Hidden Markov Model (HMM). The channel switches between ‘good’ and ‘bad’ channel situation. The transition between the sub-states of the channel is governed by a Markov Chain, which some self-return state is hidden. The performance of the model is studied by simulating probability density functions (PDF), cumulative distribution functions (CDF), level cross rate (LCR) and average fade duration (AFD).

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Kontoyiannis, I., and S. P. Meyn. "Approximating a diffusion by a finite-state hidden Markov model." Stochastic Processes and their Applications 127, no. 8 (August 2017): 2482–507. http://dx.doi.org/10.1016/j.spa.2016.11.004.

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Sun, D. X., L. Deng, and C. F. J. Wu. "State-dependent time warping in the trended hidden Markov model." Signal Processing 39, no. 3 (September 1994): 263–75. http://dx.doi.org/10.1016/0165-1684(94)90089-2.

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Young, S. J., and P. C. Woodland. "State clustering in hidden Markov model-based continuous speech recognition." Computer Speech & Language 8, no. 4 (October 1994): 369–83. http://dx.doi.org/10.1006/csla.1994.1019.

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Celeux, Gilles, and Jean-Baptiste Durand. "Selecting hidden Markov model state number with cross-validated likelihood." Computational Statistics 23, no. 4 (December 7, 2007): 541–64. http://dx.doi.org/10.1007/s00180-007-0097-1.

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Wei, Xiu Mei, Xue Song Jiang, and Xin Gang Wang. "Research of IOT Intrusion Detection System Based on Hidden Markov Model." Applied Mechanics and Materials 263-266 (December 2012): 2949–52. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2949.

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Along with the development of Internet of Things (IOT), there are a lot of increasingly serious security problems. The traditional intrusion detection method cannot adapt to the requirement of IOT. In this paper we advance a new intrusion detection method which can adapt to IOT. It is based on Hidden Markov Model (HMM), which is named as Hidden Markov state time delay sequence embedding (HMMSTdse) method.

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Ryou, Hosun, Han Hee Bae, Hee Soo Lee, and Kyong Joo Oh. "Momentum Investment Strategy Using a Hidden Markov Model." Sustainability 12, no. 17 (August 28, 2020): 7031. http://dx.doi.org/10.3390/su12177031.

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There has been a growing demand for portfolio management using artificial intelligence (AI). To sustain a competitive advantage for portfolio management, stock market investors require a strategic investment decision that can realize better returns. In this study, we propose a momentum investment strategy that employs a hidden Markov model (HMM) to select stocks in the rising state. We construct an HMM momentum portfolio that includes 890 Korean stocks and analyze the performance of the stocks over the period of January 2000 to December 2018. By identifying states of stocks, sectors, and markets through HMM, our strategy buys shares in the rising state and proceeds with rebalancing after the holding period. The HMM momentum portfolio is determined to earn higher returns than traditional momentum portfolios and to achieve the best performance under the conditions of a short holding period (one week) and a short formation period (one month). In addition, our strategy exhibits competitive performance in market and sector index investment compared with market returns. This study implies that the momentum investment strategy using HMM is useful in the Korean stock market. Based on our HMM momentum strategy, future research can be enriched by applying the HMM to developing a new AI momentum strategy that can be utilized for other portfolios containing various types of financial assets on the global market.

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