1. Detecting Time Series Motifs Under
Uniform Scaling
D. Yankov, E. Keogh, J. Medina, B. Chiu, V. Zordan
Dept. of Computer Science & Eng.
University of California Riverside

2. Outline Problem definition Motivation Formalization and approach
Experimental evaluation

3. Problem definition
Given is a long time series or a data set of shorter sequences
Goal:
Detect similar
patterns of
various scaling
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4. Motivation Object recognition with time series representation
Animation

5. Motivation (cont) Time series sampled at different rate
Physiological time series of different frequencies

6. Similarity under uniform scaling
Formalization
Similarity under uniform scaling
Motifs under uniform scaling

7. Approach
Observation: only a limited set of scaling factors need to be checked
Algorithm.
For every scaling factor do:
rescale all query subsequences
represent all time series as equal length words over the same alphabet (apply SAX)

8. Approach (cont)
Using PROJECTION (a locality sensitive hashing approach), filter out all non-matching words.
Compute the distance between the unfiltered time series pairs.

9. Experimental evaluation
Brain activity time series
Valuable in predicting epileptic seizure periods.

10. Experimental evaluation
Effectiveness of the algorithm
Efficiency

11. Experimental evaluation (cont)
Projectile shapes
Lampasas River Cornertang
Castroville Cornertang
The algorithm detects a rare cornertang segment – an object that has long intrigued anthropologists. 50
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12. Experimental evaluation (cont)
Motion-capture motifs
On this sequence the method detects the same blocking movement performed by the actor. The Euclidean distance fails to detect this motif.

13. Conclusion
Uniform scaling motifs appear in diverse areas as – animation, object recognition, medical sequence mining, etc.
The presented probabilistic approach for mining such motifs is accurate and extremely effective.
The method works in an entirely unsupervised way, requiring only a specified motif length.
Possible extensions – multivariate time series, disk resident modifications.

14. Poster# 28
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