Assessing the performance of model-based clustering methods in multivariate time series with application to identifying regional wind regimes

DOI10.1007/s13253-015-0203-8zbMath1325.62213OpenAlexW1993014035MaRDI QIDQ893352

Karen Kazor, Amanda S. Hering

Publication date: 19 November 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-015-0203-8

zbMATH Keywords

forecastingMarkov-switching modelsGaussian mixture models\(k\)-meansnonparametric mixture models

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (4)

Markov-switching linked autoregressive model for non-continuous wind direction data ⋮ Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature ⋮ A Non‐Gaussian Spatio‐Temporal Model for Daily Wind Speeds Based on a Multi‐Variate Skew‐t Distribution ⋮ Spatio-temporal short-term wind forecast: a calibrated regime-switching method

Uses Software

• depmix
• depmixS4

Cites Work

• Space-time wind speed forecasting for improved power system dispatch
• A multivariate hidden Markov model for the identification of sea regimes from incomplete skewed and circular time series
• Analysis of time series subject to changes in regime
• Markov-switching vector autoregressions. Modelling, statistical inference, and application to business cycle analysis
• Nonparametric regression with correlated errors.
• Sparse vector Markov switching autoregressive models. Application to multivariate time series of temperature
• Finite mixture and Markov switching models.
• Modern Multivariate Statistical Techniques
• How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
• Model-Based Gaussian and Non-Gaussian Clustering
• Some automated methods of smoothing time-dependent data
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models
• Powering Up With Space-Time Wind Forecasting
• An Adaptive Markov Chain Monte Carlo Approach to Time Series Clustering of Processes with Regime Transition Behavior
• Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center

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