Please use scholar(schoolar.google.com) to find other available versions of the following science direct.com examples. The best use of HMM should be in clustering or regime switching models in Time series/volatility prediction as in the last two examples

Parallel event simulations help in voluminous data crunching and AI tech like ACO etc. 

Zhibiao Zhao, Nonparametric model validations for hidden Markov models with applications in financial econometrics, Journal of Econometrics, Volume 162, Issue 2, June 2011, Pages 225-239, ISSN 0304-4076, http://dx.doi.org/10.1016/j.jeconom.2011.01.002.

(http://www.sciencedirect.com/science/article/pii/S0304407611000042)

Abstract: We address the nonparametric model validation problem for hidden Markov models with partially observable variables and hidden states. We achieve this goal by constructing a nonparametric simultaneous confidence envelope for transition density function of the observable variables and checking whether the parametric density estimate is contained within such an envelope. Our specification test procedure is motivated by a functional connection between the transition density of the observable variables and the Markov transition kernel of the hidden states. Our approach is applicable for continuous-time diffusion models, stochastic volatility models, nonlinear time series models, and models with market microstructure noise.

Keywords: Confidence envelope; Diffusion model; Hidden Markov model; Market microstructure noise; Model validation; Nonlinear time series; Transition density; Stochastic volatility

Elise Epaillard, Nizar Bouguila, Proportional data modeling with hidden Markov models based on generalized Dirichlet and Beta-Liouville mixtures applied to anomaly detection in public areas, Pattern Recognition, Volume 55, July 2016, Pages 125-136, ISSN 0031-3203, http://dx.doi.org/10.1016/j.patcog.2016.02.004.

(http://www.sciencedirect.com/science/article/pii/S0031320316000601)

Abstract: Abstract

The recent and rapid deployment of CCTV cameras in public areas invokes the need for a capability to assist human operators in the real-time detection of threats and anomalous events. The main difficulty in this regard is the rare and unpredictable nature of the events to be detected. A typical strategy consists in modeling situations considered as normal and detecting outliers to this model. In this paper, we theoretically derive two variants of the hidden Markov model for proportional data modeling and assess their performance for unusual event detection in several public surveillance situations. Proportional data arise in a number of pattern recognition applications and classic hidden Markov models are not well adapted so far for their specific processing. Our models are based on the use of the generalized Dirichlet and Beta-Liouville distributions as emission probability functions and yield enhanced performance compared to the use of the Dirichlet distribution.

Keywords: Hidden Markov models; Generalized Dirichlet; Beta-Liouville; Proportional data; Anomaly detection; Public security

Nikolaus Mutsam, Franz Pernkopf, Maximum Margin Hidden Markov Models for Sequence Classification, Pattern Recognition Letters, Available online 30 March 2016, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2016.03.017.

(http://www.sciencedirect.com/science/article/pii/S0167865516300216)

Abstract: Abstract

Discriminative learning methods are known to work well in pattern classification tasks and often show benefits compared to generative learning. This is particularly true in case of model mismatch, i.e. the model cannot represent the true data distribution. In this paper, we derive discriminative maximum margin learning for hidden Markov models (HMMs) with emission probabilities represented by Gaussian mixture models (GMMs). The focus is on single-label sequence classification where the margin objective is specified by the probabilistic gap between the true class and the most competing class. In particular, we use the extended Baum-Welch (EBW) framework to optimize this probabilistic margin embedded in a hinge loss function. Approximations of the margin objective and the derivatives are necessary. In the experiments, we compare maximum margin HMMs to generative maximum likelihood and discriminative conditional log-likelihood (CLL) HMM training. We present results of classifying trajectories of handwritten characters, Australian sign language data, digits of speech data and UCR time-series data. Maximum margin HMMs outperform in many cases CLL-HMMs. Furthermore, maximum margin HMMs achieve a significantly better performance than generative maximum likelihood HMMs.

Keywords: Hidden Markov models; Discriminative learning; Maximum margin learning

Hedieh Rashidi Ranjbar, Abbas Seifi, A path-independent method for barrier option pricing in hidden Markov models, Physica A: Statistical Mechanics and its Applications, Volume 440, 15 December 2015, Pages 1-8, ISSN 0378-4371, http://dx.doi.org/10.1016/j.physa.2015.08.003.

(http://www.sciencedirect.com/science/article/pii/S037843711500655X)

Abstract: Abstract

This paper presents a method for barrier option pricing under a Black–Scholes model with Markov switching. We extend the option pricing method of Buffington and Elliott to price continuously monitored barrier options under a Black–Scholes model with regime switching. We use a regime switching random Esscher transform in order to determine an equivalent martingale pricing measure, and then solve the resulting multidimensional integral for pricing barrier options. We have calculated prices for down-and-out call options under a two-state hidden Markov model using two different Monte-Carlo simulation approaches and the proposed method. A comparison of the results shows that our method is faster than Monte-Carlo simulation methods.

Keywords: Barrier option pricing; Markov-switching; Esscher transform; Monte-Carlo simulation

http://www.sciencedirect.com/science/article/pii/S037843711500655X

José G. Dias, Jeroen K. Vermunt, Sofia Ramos, Clustering financial time series: New insights from an extended hidden Markov model, European Journal of Operational Research, Volume 243, Issue 3, 16 June 2015, Pages 852-864, ISSN 0377-2217, http://dx.doi.org/10.1016/j.ejor.2014.12.041.

(http://www.sciencedirect.com/science/article/pii/S0377221714010595)

Abstract: Abstract

In recent years, large amounts of financial data have become available for analysis. We propose exploring returns from 21 European stock markets by model-based clustering of regime switching models. These econometric models identify clusters of time series with similar dynamic patterns and moreover allow relaxing assumptions of existing approaches, such as the assumption of conditional Gaussian returns. The proposed model handles simultaneously the heterogeneity across stock markets and over time, i.e., time-constant and time-varying discrete latent variables capture unobserved heterogeneity between and within stock markets, respectively. The results show a clear distinction between two groups of stock markets, each one characterized by different regime switching dynamics that correspond to different expected return-risk patterns. We identify three regimes: the so-called bull and bear regimes, as well as a stable regime with returns close to 0, which turns out to be the most frequently occurring regime. This is consistent with stylized facts in financial econometrics.

Keywords: Data mining; Hidden Markov model; Stock indexes; Latent class model; Regime-switching model

http://www.sciencedirect.com/science/article/pii/S0304407611000042

http://www.sciencedirect.com/science/article/pii/S0031320316000601

http://www.sciencedirect.com/science/article/pii/S037843711500655X

http://www.sciencedirect.com/science/article/pii/S0377221714010595

thank you Mr mittal.

I have implemented a parallel (using openMP)  HDPHMM/HDPHMM  whiach are nonparametric HMMs. 

https://www.researchgate.net/publication/283889476_A_Doubly_Hierarchical_Dirichlet_Process_Hidden_Markov_Model_with_a_Non-Ergodic_Structure

http://amir1981.github.io/hdphmm_lib/

Article A Doubly Hierarchical Dirichlet Process Hidden Markov Model ...

Thank you Mr Harati