Advances in clustering and visualization of time series using GTM through time

DOI10.1016/j.neunet.2008.05.013zbMath1254.68219OpenAlexW2094187853WikidataQ40066707 ScholiaQ40066707MaRDI QIDQ1932044

Iván Olier, Alfredo Vellido

Publication date: 17 January 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2008.05.013

zbMATH Keywords

visualizationclusteringmultivariate time serieschange point detectiongenerative topographic mappingunsupervised relevance determination

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Uses Software

• GTM

Cites Work

• Unnamed Item
• Missing data imputation through GTM as a mixture of \(t\)-distributions
• Developments of the generative topographic mapping
• Latent variable models for the topographic organisation of discrete and strictly positive data
• On the generative probability density model in the self-organizing map
• Unfolding preprocessing for meaningful time series clustering
• SOM-based data visualization methods
• Dynamics and Topographic Organization of Recursive Self-Organizing Maps
• 10.1162/153244303322753634
• An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology
• Self-organizing maps.