Proximity Forest: an effective and scalable distance-based classifier for time series

• Published in: Data mining and knowledge discovery Vol. 33; no. 3; pp. 607 - 635
• Main Authors: Lucas, Benjamin, Shifaz, Ahmed, Pelletier, Charlotte, O’Neill, Lachlan, Zaidi, Nayyar, Goethals, Bart, Petitjean, François, Webb, Geoffrey I.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2019; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Archives & records; Artificial Intelligence; Chemistry and Earth Sciences; Classification; Classifiers; Computer Science; Data Mining and Knowledge Discovery; Datasets; Decision trees; Information Storage and Retrieval; Physics; Proximity; State of the art; Statistics for Engineering; Time series; Time series classification; Scalable classification; Time-warp similarity measures; Ensemble

Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10,000  tim...