catch22: CAnonical Time-series CHaracteristics Selected through highly comparative time-series analysis

• Published in: Data mining and knowledge discovery Vol. 33; no. 6; pp. 1821 - 1852
• Main Authors: Lubba, Carl H., Sethi, Sarab S., Knaute, Philip, Schultz, Simon R., Fulcher, Ben D., Jones, Nick S.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2019
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data Mining and Knowledge Discovery; Information Storage and Retrieval; Physics; Statistics for Engineering; Time-series features; Clustering; Time series; Classification

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can b...