Hhsmm: An R package for hidden hybrid Markov/semi-Markov models

TL;DR

The paper presents the hhsmm R package for flexible hidden hybrid Markov/semi-Markov models, enabling advanced state modeling, prediction, and lifetime estimation, demonstrated through aerospace and energy demand applications.

Contribution

Introduction of the hhsmm R package with functions for modeling, fitting, and predicting hybrid Markov/semi-Markov models, including nonparametric emission estimation and real-world applications.

Findings

Effective modeling of complex state systems demonstrated on aerospace data.

Accurate prediction of energy demand using the hhsmm package.

Nonparametric emission estimation improves model flexibility.

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.