MultiETSC: automated machine learning for early time series classification

DOI10.1007/s10618-021-00781-5zbMath1485.68224OpenAlexW3193366935MaRDI QIDQ2066661

Gilles Ottervanger, Mitra Baratchi, Holger H. Hoos

Publication date: 14 January 2022

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

Full work available at URL: https://doi.org/10.1007/s10618-021-00781-5

zbMATH Keywords

automated machine learningtime-series classificationearly classification

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)

Uses Software

• Paramils
• Spearmint
• Hyperopt
• PyTorch
• Auto-WEKA
• GitHub
• SMAC
• auto-sklearn
• AutoKeras
• Autotune
• MO-ParamILS

Cites Work

• Early classification of time series using multi-objective optimization techniques
• A Multicriteria Generalization of Bayesian Global Optimization
• ParamILS: An Automatic Algorithm Configuration Framework
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