Qualitative approximation to Dynamic TimeWarping similarity between time series data

author: Blaž Strle, Faculty of Computer and Information Science, University of Ljubljana
published: July 22, 2009,   recorded: June 2009,   views: 4595


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Dynamic time warping (DTW) is a method for calculating the similarity between two time series which can occur at different times or speeds. Although its effectiveness made it very popular in several disciplines, its time complexity of O(N2) makes it useful only for relatively short time series. In this paper, we propose a qualitative approximation Qualitative Dynamic Time Warping (QDTW) to DTW. QDTW reduces a time series length by transforming it to qualitative time series. DTW is later calculated between qualitative time series. As qualitative time series are normally much shorter than their corresponding numerical time series, time to compute their similarity is significantly reduced. Experimental results have shown improved running time of up to three orders of magnitude, while prediction accuracy only slightly decreased.

See Also:

Download slides icon Download slides: qr09_strle_qad_01.ppt (2.2 MB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: