Time Series Shapelets: A New Primitive for Data Mining

1 / 27

# Time Series Shapelets: A New Primitive for Data Mining - PowerPoint PPT Presentation

Time Series Shapelets: A New Primitive for Data Mining. Lexiang Ye and Eamonn Keogh University of California, Riverside. Classification. Classification Huge interest in time series Extensive applications Nearest Neighbor Most accurate (in extensive empirical tests) Robust Simple.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Time Series Shapelets: A New Primitive for Data Mining' - debra-mcintosh

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

### Time Series Shapelets: A New Primitive for Data Mining

Lexiang Ye and Eamonn Keogh

University of California, Riverside

Classification
• Classification
• Huge interest in time series
• Extensive applications
• Nearest Neighbor
• Most accurate (in extensive empirical tests)
• Robust
• Simple
Drawback of the NN
• Time and space complexity
• Results are not interpretable
Solution
• Shapelets
• shapelets are time series subsequences which are maximally representative of a class
• Distinguishing substring selection
• Probe design (computational biology)

false nettles

stinging nettles

false nettles

Shapelet Dictionary

I

Shapelet

5.1

Leaf Decision Tree

I

yes

no

0

1

false nettles

stinging nettles

stinging nettles

false nettles

Testing the utility of a candidate shapelet
• Information gain
• Arrange the time series objects
• Find the optimal split point
• Pick the candidate achieving best utility as the shapelet

candidate

Split Point

0

Candidates Pool

Problem
• Total number of candidate
• Trace dataset
• 200 instances, each of length 275
• 7,480,200 shapelet candidates
• approximately three days

. . .

Speedup
• Distance calculations from time series objects to shapelet candidates are the most expensive part
• Reduce the time in two ways
• Distance Early Abandon (known idea)
• Admissible Entropy Pruning (novel idea)
• Information Gain
• Traditional evaluation in decision tree
• Easily generalized to the multi-class problem
• Reduce the number of distance calculations

stinging nettles

false nettles

0

I=0.42

I= 0.29

0

0

false nettles

stinging nettles

false nettles

Shapelet Dictionary

I

Shapelet

5.1

Classification

Leaf Decision Tree

I

yes

no

0

1

false nettles

stinging nettles

stinging nettles

false nettles

Performance Comparison

5 *105

1.00

Brute Force

4 *105

0.95

3 *105

seconds

accuracy

0.90

2 *105

Currently best published accuracy 91.1%

Pruning

0.85

1 *105

0

0.80

160

10

20

40

80

10

20

40

80

320

160

|D|, the number of objects in the database

|D|, the number of objects in the database

I

II

0

2

1

Avonlea

Clovis

1.0

(Clovis)

11.24

I

0

(Avonlea)

85.47

II

Shapelet Dictionary

0

200

400

Wheat Spectrography

1

0.5

0

0

200

400

600

800

1000

1200

one sample from each class

I

V

II

III

IV

VI

2

4

0

1

3

6

5

Shapelet Dictionary

I

0.4

II

0.3

III

0.2

IV

0.1

0.0

V

VI

300

0

100

200

Wheat Decision Tree

the Gun/NoGun Problem

No Gun

Gun

(No Gun)

2

38.94

I

0

Shapelet Dictionary

0

50

100

Gun Decision Tree

I

1

0

0

100

200

300

Reduces the sensitivity of alignment

1.0

0

0.909

0.902

0.860

right toe

144.075

I

left toe

(Normal Walk)

Walk Decision Tree

I

0.535

0

1

Conclusions
• Interpretable results
• more accurate/robust
• significantly faster at classification

Thank You 

Question?