Early abandoning and pruning for elastic distances including dynamic time warping

DOI10.1007/s10618-021-00782-4zbMath1491.62100arXiv2102.05221OpenAlexW3194337021MaRDI QIDQ2066658

Matthieu Herrmann, Geoffrey I. Webb

Publication date: 14 January 2022

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

Full work available at URL: https://arxiv.org/abs/2102.05221

zbMATH Keywords

time serieselastic distances

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• NumPy
• GitHub
• UCR Suite
• TS-CHIEF

Cites Work

• Unnamed Item
• A global averaging method for dynamic time warping, with applications to clustering
• Time series classification with ensembles of elastic distance measures
• Speeding up similarity search under dynamic time warping by pruning unpromising alignments
• TS-CHIEF: a scalable and accurate forest algorithm for time series classification
• Time series extrinsic regression. Predicting numeric values from time series data
• FastEE: fast ensembles of elastic distances for time series classification
• Faster retrieval with a two-pass dynamic-time-warping lower bound
• Dynamic programming algorithm optimization for spoken word recognition

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