An Adaptive Markov Chain Monte Carlo Approach to Time Series Clustering of Processes with Regime Transition Behavior

DOI10.1137/120881981OpenAlexW2082679121MaRDI QIDQ5417550

Jana de Wiljes, Andrew J. Majda, Illia Horenko

Publication date: 21 May 2014

Published in: Multiscale Modeling & Simulation (Search for Journal in Brave)

Full work available at URL: https://semanticscholar.org/paper/58231fcd10c88162003370e66779f85cf339c3c5

zbMATH Keywords

regularization clustering Markov chain Monte Carlo time series analysis nonstationarity

Mathematics Subject Classification ID

Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).

Related Items (4)

Statistical Learning of Nonlinear Stochastic Differential Equations from Nonstationary Time Series using Variational Clustering ⋮ Information theory, model error, and predictive skill of stochastic models for complex nonlinear systems ⋮ Assessing the performance of model-based clustering methods in multivariate time series with application to identifying regional wind regimes ⋮ Statistical Regression Analysis of Threshold Excesses with Systematically Missing Covariates

This page was built for publication: An Adaptive Markov Chain Monte Carlo Approach to Time Series Clustering of Processes with Regime Transition Behavior