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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

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Outline

• Problem definition

• Motivation

• Formalization and approach

• Experimental evaluation

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Problem definition

• Given is a long time series or a data set of shorter sequences

• Goal: Detect similar patterns of various scaling

0 100 200 300 400 500 600

A

C

BA

B

0 100 200 300 400 500 600

A C B

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Motivation

• Object recognition with time series representation

• Animation

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Motivation (cont)

• Time series sampled at different rate

• Physiological time series of different frequencies

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Formalization

• Similarity under uniform scaling

• Motifs under uniform scaling

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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)

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Approach (cont)– Using PROJECTION (a locality sensitive hashing

approach), filter out all non-matching words.

– Compute the distance between the unfiltered time series pairs.

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Experimental evaluation

• Brain activity time series

Valuable in predicting epileptic seizure periods.

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Experimental evaluation

• Effectiveness of the algorithm

• Efficiency

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Experimental evaluation (cont)

• Projectile shapes

Lampasas River Cornertang

Castroville Cornertang

0 50 100 150 200 250 300 350

The algorithm detects a rare cornertang segment – an object that has long intrigued anthropologists.

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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.

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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.

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Poster# 28

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