End-to-end deep representation learning for time series clustering: a comparative study

DOI10.1007/s10618-021-00796-yzbMath1494.68242OpenAlexW3205581618WikidataQ125121836 ScholiaQ125121836MaRDI QIDQ832639

Jonathan Weber, Pierre Gançarski, Baptiste Lafabregue, Germain Forestier

Publication date: 25 March 2022

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

Full work available at URL: https://doi.org/10.1007/s10618-021-00796-y

zbMATH Keywords

time seriesclusteringdeep learning

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial neural networks and deep learning (68T07)

Uses Software

• ImageNet
• FaceNet
• t-SNE
• Tensor2Tensor
• UMAP
• Grad-CAM
• rocket
• AlexNet
• ClusterGAN
• CBAM

Cites Work

• Deep learning for time series classification: a review
• A global averaging method for dynamic time warping, with applications to clustering
• ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
• Clustering of time series data -- a survey
• Dynamic programming algorithm optimization for spoken word recognition
• Fast and Accurate Time-Series Clustering
• Neural networks and physical systems with emergent collective computational abilities.
• Learning representations by back-propagating errors
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