A hidden Markov model with dependence jumps for predictive modeling of multidimensional time-series

DOI10.1016/J.INS.2017.05.038zbMath1435.68282OpenAlexW2619967634MaRDI QIDQ778379

Stelios Xanthopoulos, Sotirios P. Chatzis, Anastasios Petropoulos

Publication date: 2 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2017.05.038

zbMATH Keywords

hidden Markov modelsexpectation-maximizationvariable orderdependence jumpstemporal dynamics

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Economic time series analysis (91B84) Learning and adaptive systems in artificial intelligence (68T05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20)

Cites Work

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• Efficient backward decoding of high-order hidden Markov models
• Hidden semi-Markov models
• Stylized facts of financial time series and hidden semi-Markov models
• A conditional random field-based model for joint sequence segmentation and classification
• Variable length Markov chains
• Moving horizon estimation for Markov jump systems
• Inference in hidden Markov models.
• Volatility Jumps
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Finite mixture models
• Empirical properties of asset returns: stylized facts and statistical issues
• Random forests
• Variational inference for Dirichlet process mixtures

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