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Ratanamahatana, C.A. & Keogh, E. (2004). ‘Everything You Know About Dynamic Time Warping Is Wrong’ In The Tenth ACM SIGKDD Inter.pdf

DTW_myths.pdf
Preview of Ratanamahatana, C.A. & Keogh, E. (2004). ‘Everything you know about dynamic time warping is wrong’ in the Tenth ACM SIGKDD Inter
🔗 Source: cs.ucr.edu
📊 Size: 1.45 MB
👤 Author: Keogh, E., Lonardi, S., Ratanamahatana, C.A.
⬇️ Downloads: 168

Summary

Dynamic Time Warping (DTW) is a technique used in speech recognition and data mining to measure similarity between two time series by minimizing the distance between them through non-linear mapping. Despite its success, several myths about DTW have caused confusion and wasted research effort. The three major myths are: (1) DTW's ability to handle sequences of different lengths is a great advantage, (2) constraining the warping paths is necessary, and (3) there is a need for improvements in DTW's speed. However, empirical evidence suggests that comparing sequences of different lengths and reinterpolating them to equal length produce no statistically significant difference in accuracy, the 10% constraint on warping is too large for real-world data mining, and DTW is essentially O(n) for data mining applications using a simple lower bounding technique.

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Description

Dynamic Time Warping (DTW) is a technique used in speech recognition and data mining to measure similarity between two time series by minimizing the distance...

Technical Information

  • File Format: PDF
  • File Size: 1.45 MB
  • Pages: 11
  • Language: EN
  • Author: Keogh, E., Lonardi, S., Ratanamahatana, C.A.
  • Total Downloads: 168
  • Last Updated: 3 days ago

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