Paper Info
Title
Dynamic Time Warping Averaging of Time Series Allows Faster and More Accurate Classification
Abstract
Recent years have seen significant progress in improving both the efficiency and effectiveness of time series classification. However, because the best solution is typically the Nearest Neighbor algorithm with the relatively expensive Dynamic Time Warping as the distance measure, successful deployments on resource constrained devices remain elusive. Moreover, the recent explosion of interest in wearable devices, which typically have limited computational resources, has created a growing need for very efficient classification algorithms. A commonly used technique to glean the benefits of the Nearest Neighbor algorithm, without inheriting its undesirable time complexity, is to use the Nearest Centroid algorithm. However, because of the unique properties of (most) time series data, the centroid typically does not resemble any of the instances, an unintuitive and underappreciated fact. In this work we show that we can exploit a recent result to allow meaningful averaging of 'warped' times series, and that this result allows us to create ultra-efficient Nearest 'Centroid' classifiers that are at least as accurate as their more lethargic Nearest Neighbor cousins.
Year
DOI
Venue
2014
10.1109/ICDM.2014.27
ICDM
Keywords
Field
DocType
classification algorithms,artificial neural networks,accuracy,time series analysis,prototypes
Time series,Data mining,Dynamic time warping,Best bin first,Computer science,Artificial intelligence,Artificial neural network,Time complexity,k-nearest neighbors algorithm,Pattern recognition,Statistical classification,Centroid,Machine learning
Conference
ISSN
Citations 
PageRank 
1550-4786
51
1.64
References 
Authors
20
6
Name
Order
Citations
PageRank
François Petitjean147434.26
germain forestier246742.14
Geoffrey I. Webb33130234.10
Ann E. Nicholson469288.01
Yanping Chen520817.42
Eamonn J. Keogh611859645.93