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Lecture 10.1 Time Series (Dis)Similarity (Dynamic Time Warping)

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. 25 th 2019. Similarity between two time series. Signal A:. Signal B:. Similarity between two time series: Euclidian distance.

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Lecture 10.1 Time Series (Dis)Similarity (Dynamic Time Warping)

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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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