Learning a symbolic representation for multivariate time series classification

DOI10.1007/s10618-014-0349-yzbMath1403.68181OpenAlexW2066224818MaRDI QIDQ1711227

Mustafa Gokce Baydogan, George C. Runger

Publication date: 17 January 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-014-0349-y

zbMATH Keywords

codebooksupervised learningdecision trees

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Distance measure with improved lower bound for multivariate time series, ABBA: adaptive Brownian bridge-based symbolic aggregation of time series, Time series representation and similarity based on local autopatterns, Generalized random shapelet forests, Deep learning for time series classification: a review, Time series extrinsic regression. Predicting numeric values from time series data

Uses Software

• UCI-ml
• C4.5
• iSAX
• CMU Motion Capture

Cites Work

• On the time series support vector machine using dynamic time warping kernel for brain activity classification
• Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification
• Classification of multivariate time series and structured data using constructive induction
• Multivariate decision trees
• Component-based discriminative classification for hidden Markov models
• Dynamic programming algorithm optimization for spoken word recognition
• Random forests
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