Partially hidden Markov chain multivariate linear autoregressive model: inference and forecasting -- application to machine health prognostics
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Publication:6097139
DOI10.1007/s10994-022-06209-5OpenAlexW4310125547MaRDI QIDQ6097139
Christine Sinoquet, Fatoumata Dama
Publication date: 12 June 2023
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-022-06209-5
time series analysisforecastingautoregressive modelregime-switching modelhidden state inferencemachine health prognostics
Cites Work
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- Markov-switching model selection using Kullback-Leibler divergence
- A sticky HDP-HMM with application to speaker diarization
- Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?
- Analysis of time series subject to changes in regime
- Estimating the dimension of a model
- Dynamic linear models with Markov-switching
- Selecting the number of states in hidden Markov models: pragmatic solutions illustrated using animal movement
- Non-homogeneous hidden Markov-switching models for wind time series
- Distribution of the Estimators for Autoregressive Time Series With a Unit Root
- Joint Determination of the State Dimension and Autoregressive Order for Models with Markov Regime Switching
- ON THE DETERMINATION OF THE NUMBER OF REGIMES IN MARKOV-SWITCHING AUTOREGRESSIVE MODELS
- Testing for a unit root in time series regression
- A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
- A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
- A new look at the statistical model identification
- A comparison of the forecast performance of Markov‐switching and threshold autoregressive models of US GNP