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Time Series (Dis)Similarity: Dynamic Time Warping in Applications

Understand the similarity between two time series using Dynamic Time Warping (DTW) and Euclidean distance. Explore DTW complexity, constraints, and preprocessing techniques in this lecture. Includes examples and reference materials.

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Time Series (Dis)Similarity: Dynamic Time Warping in Applications

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  1. Lecture 10.1 Time Series (Dis)Similarity (Dynamic Time Warping) CMSC 818W : Spring 2019 Tu-Th 2:00-3:15pm CSI 2118 Nirupam Roy Apr. 25th 2019

  2. Similarity between two time series Signal A: Signal B:

  3. Similarity between two time series: Euclidian distance Sqrt((a1-b1)2 + (a2-b2)2 ) (a1, a2) (b1, b2)

  4. Similarity between two time series: Euclidian distance Signal A: N number of points Signal B: Euclidian distance:

  5. Similarity between two time series: Euclidian distance Signal A: N number of points Signal B: Euclidian distance:

  6. Similarity between two time series: Euclidian distance Euclidian distance

  7. Similarity between two time series: Euclidian distance Desired approach

  8. Similarity between two time series: Euclidian distance Desired approach

  9. Similarity between two time series: Applications

  10. Similarity between two time series: Applications Citation:

  11. Similarity between two time series: Applications

  12. Dynamic Time Warping

  13. Dynamic Time Warping Slides by Quim LlimonaTorrashttps://lemonzi.files.wordpress.com/2013/01/dtw.pdf

  14. DTW : An example [Citation] Slides take from Thales Sehn Körting https://www.youtube.com/watch?v=_K1OsqCicBY

  15. |Ai-Bj| + min{ D[i-1,j-1], D[i-1,j], D[i,j-1]} |9-3| + min{ 5, 5, 11} = 6 + 5 = 11 A i B j

  16. min{ D[i-1,j-1], D[i-1,j], D[i,j-1]} A i B j

  17. min{ D[i-1,j-1], D[i-1,j], D[i,j-1]} A i B j

  18. DTW : An example

  19. min{ D[i-1,j-1], D[i-1,j], D[i,j-1]} A i B j

  20. DTW : An example

  21. DTW : An example

  22. DTW : An example

  23. DTW : Recap Reference: http://www.cs.ucr.edu/~eamonn/KAIS_2004_warping.pdf

  24. DTW : Recap

  25. DTW : Recap |Ai-Bj| + min{ D[i-1,j-1], D[i-1,j], D[i,j-1]} |9-3| + min{ 5, 5, 11} = 6 + 5 = 11 A i B j

  26. min{ D[i-1,j-1], D[i-1,j], D[i,j-1]} A i B j

  27. DTW : An example

  28. DTW : Complexity What is the complexity of DTW algorithm?

  29. DTW : Constraints Slides by Quim LlimonaTorrashttps://lemonzi.files.wordpress.com/2013/01/dtw.pdf

  30. DTW : Warping Window

  31. DTW : Preprocessing

  32. DTW : Preprocessing – Remove DC offset

  33. DTW : Preprocessing – Adjust amplitude

  34. DTW : Preprocessing – Remove linear trend

  35. Extra slides

  36. A i B j

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