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hhsmm: an R package for hidden hybrid Markov/semi-Markov models

Author

Listed:
  • Morteza Amini

    (University of Tehran)

  • Afarin Bayat

    (University of Tehran)

  • Reza Salehian

    (University of Tehran)

Abstract

This paper introduces the hhsmm R package, which involves functions for initializing, fitting, and predication of hidden hybrid Markov/semi-Markov models. These models are flexible models with both Markovian and semi-Markovian states, which are applied to situations where the model involves absorbing or macro-states. The left-to-right models and the models with series/parallel networks of states are two models with Markovian and semi-Markovian states. The hhsmm also includes Markov/semi-Markov switching regression model as well as the auto-regressive HHSMM, the nonparametric estimation of the emission distribution using penalized B-splines, prediction of future states and the residual useful lifetime estimation in the predict function. The commercial modular aero-propulsion system simulation (C-MAPSS) data-set is also included in the package, which is used for illustration of the application of the package features. The application of the hhsmm package to the analysis and prediction of the Spain’s energy demand is also presented.

Suggested Citation

  • Morteza Amini & Afarin Bayat & Reza Salehian, 2023. "hhsmm: an R package for hidden hybrid Markov/semi-Markov models," Computational Statistics, Springer, vol. 38(3), pages 1283-1335, September.
    • Handle: RePEc:spr:compst:v:38:y:2023:i:3:d:10.1007_s00180-022-01248-x
    DOI: 10.1007/s00180-022-01248-x
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    References listed on IDEAS

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    1. Roland Langrock & Thomas Kneib & Alexander Sohn & Stacy L. DeRuiter, 2015. "Nonparametric inference in hidden Markov models using P-splines," Biometrics, The International Biometric Society, vol. 71(2), pages 520-528, June.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Bulla, Jan & Bulla, Ingo & Nenadic, Oleg, 2010. "hsmm -- An R package for analyzing hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 611-619, March.
    4. Timo Adam & Roland Langrock & Christian H. Weiß, 2019. "Penalized estimation of flexible hidden Markov models for time series of counts," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 87-104, August.
    5. Christian Schellhase & Göran Kauermann, 2012. "Density estimation and comparison with a penalized mixture approach," Computational Statistics, Springer, vol. 27(4), pages 757-777, December.
    6. Francesco Cartella & Jan Lemeire & Luca Dimiccoli & Hichem Sahli, 2015. "Hidden Semi-Markov Models for Predictive Maintenance," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-23, February.
    7. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
    8. Visser, Ingmar & Speekenbrink, Maarten, 2010. "depmixS4: An R Package for Hidden Markov Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i07).
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