Optimizing dynamic time warping’s window width for time series data mining applications

• Published in: Data mining and knowledge discovery Vol. 32; no. 4; pp. 1074 - 1120
• Main Authors: Dau, Hoang Anh, Silva, Diego Furtado, Petitjean, François, Forestier, Germain, Bagnall, Anthony, Mueen, Abdullah, Keogh, Eamonn
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2018; Springer Nature B.V; Springer
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Classification; Clustering; Computer Science; Data mining; Data Mining and Knowledge Discovery; Data structures; Datasets; Distance measurement; Information Storage and Retrieval; Other; Parameters; Physics; Statistics for Engineering; Time series; Training; Warping; Windows (intervals); Clustering; Semi-supervised learning; Dynamic time warping; Time series; Classification

Dynamic Time Warping (DTW) is a highly competitive distance measure for most time series data mining problems. Obtaining the best performance from DTW requires setting its only parameter, the maximum amount of warping ( w ). In the supervised case with ample data, w is typically set by cross-validat...