Scaling and time warping in time series querying

  • Authors:

  • Ada Wai-Chee Fu;Eamonn Keogh;Leo Yung Lau;Chotirat Ann Ratanamahatana;Raymond Chi-Wing Wong

  • Affiliations:

  • The Chinese University of Hong Kong, Shatin, Hong Kong;University of California, Riverside, USA;The Chinese University of Hong Kong, Shatin, Hong Kong;Computer Engineering Department, Chulalongkorn University, Bangkok, Thailand;The Chinese University of Hong Kong, Shatin, Hong Kong

  • Venue:
  • The VLDB Journal — The International Journal on Very Large Data Bases
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

The last few years have seen an increasing understanding that dynamic time warping (DTW), a technique that allows local flexibility in aligning time series, is superior to the ubiquitous Euclidean distance for time series classification, clustering, and indexing. More recently, it has been shown that for some problems, uniform scaling (US), a technique that allows global scaling of time series, may just be as important for some problems. In this work, we note that for many real world problems, it is necessary to combine both DTW and US to achieve meaningful results. This is particularly true in domains where we must account for the natural variability of human actions, including biometrics, query by humming, motion-capture/animation, and handwriting recognition. We introduce the first technique which can handle both DTW and US simultaneously, our techniques involve search pruning by means of a lower bounding technique and multi-dimensional indexing to speed up the search. We demonstrate the utility and effectiveness of our method on a wide range of problems in industry, medicine, and entertainment.