Faster and more accurate classification of time series by exploiting a novel dynamic time warping averaging algorithm

—A concerted research effort over the past two decades has heralded significant improvements in both the efficiency and effectiveness of time series classification. The consensus that has emerged in the community is that the best solution is a surprisingly simple one. In virtually all domains, the most accurate classifier is the Nearest Neighbor algorithm with Dynamic Time Warping as the distance measure. The time-complexity of Dynamic Time Warping means that successful deployments on resource constrained devices remain elusive. Moreover, the recent explosion of interest in wearable computing devices, which typically have limited computational resources, has greatly increased the need for very efficient classification algorithms. A classic technique to obtain the benefits of the Nearest Neighbor algorithm, without inheriting its undesirable time and space complexity, is to use the Nearest Centroid algorithm. Unfortunately, the unique properties of (most) time series data mean that th...

• Type: Journal
• Year: 2016
• Where: KAIS
• Authors: François Petitjean, Germain Forestier, Geoffrey I. Webb, Ann E. Nicholson, Yanping Chen 0005, Eamonn J. Keogh
• Keywords: KAIS 2016