Partially Hidden Markov Chain Linear Autoregressive model: inference and forecasting

Autor: Dama, Fatoumata, Sinoquet, Christine

Time series subject to change in regime have attracted much interest in domains such as econometry, finance or meteorology. For discrete-valued regimes, some models such as the popular Hidden Markov Chain (HMC) describe time series whose state proces

Externí odkaz: http://arxiv.org/abs/2102.12584

Prediction and Inference in a Partially Hidden Markov-switching Framework with Autoregression. Application to Machinery Health Diagnosis

Autor: Dama, Fatoumata, Sinoquet, Christine

Publikováno v: 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), IEEE-CS, Nov 2021, virtual event, United States

International audience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a3eb8ee3a41fb35839fea12f75041ea2
https://hal.archives-ouvertes.fr/hal-03345260

Making use of partially observed states in Markov switching autoregressive models: application to machine health diagnosis

Autor: Dama, Fatoumata, Sinoquet, Christine

Publikováno v: Actes CNIA PFIA 2021
CNIA 2021 : Conférence Nationale en Intelligence Artificielle
CNIA 2021 : Conférence Nationale en Intelligence Artificielle, Jun 2021, Bordeaux (en ligne), France. pp 14-21

International audience; Machine health diagnosis is a fundamental task dedicated to monitor systems’ safety in order to prevent incidents, and to program maintenance operations. Such diagnosis is achieved through analyzing the system’s features (

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::7f6ffae568f991e05cfab937ff974c71
https://hal.archives-ouvertes.fr/hal-03321148