novel online methods for time series segmentation

Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Novel Online Methods for Time Series Segmentation

Xiaoyan Liu, Zhenjiang Lin, and Huaiqing Wang

TKDE, Vol. 20, No. 12, 2008, pp. 1616-1626.

Presenter : Wei-Shen Tai

2009/1/20

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Outline Introduction Related work Novel online segmentation algorithms: FSW &

SFSW Complexity analysis Experiments Conclusions Comments

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Motivation Represent a time series approximately in a few segments

Representation quality : minimizing the representation error as possible. Computing efficiency : fast enough to fit for an online real-time working

environment.

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Objective Novel online segmentation algorithms

Efficiently finds the farthest endpoint of a segment and reduces the representation error for dealing with an online data sequence.

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Sliding window method Classic SW

Interpolating line or regression line between the two endpoints of the segment is used as the approximation.

t1 t2 t3 t4

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Segmentation criterion

Evaluation for the goodness of fit line in segmentation methods Regression line

Residual error Interpolation line

MVD: sum of the squares of vertical distances between actual data points and the best fit line. Maximum error tolerance

A user-specified maximum error tolerance δ.

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Feasible Space Window (FSW) Candidate Segmenting Point (CSP)

Chosen to be the next eligible segmenting point. FSW

Searches for the farthest CSP to make the current segment as long as possible under the given maximum error tolerance.

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Stepwise Feasible Space Window (SFSW)

SW method Lacks an overall view of the whole time series.

SFSW Backward FSW to find a backward segmenting end point. Find the optimal segmenting point in the interval of both

forward and backward end points.

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Complexity analysis Comparison between SW methods

Given time series T of n data points, the number of segments and the average segment length are denoted by K and L, respectively.

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Experiments

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Conclusions FSW

Reduces the number of segments by searching for the farthest endpoint of a potential segment.

SFSW Refines the segmenting points by taking into account the

effects of new incoming points so that the representation error can be reduced.

Future works An amnesic representation, varying stepwise method,

continuous feature discreteziation, multidimensional time series.

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Comments Advantage

This method superiors to other SW methods in that it can consider the more global view of time series via backward FSW.

The user-specified threshold δ and feasible space concept make this method become an incremental segmentation algorithm.

Drawback FSW is an quite efficient method but its representation error is larger than other

SW methods. SFSW can reduce both the number of segment and representation error but

increase its computation complexity also. Application

Time series segmentation.

novel online methods for time series segmentation

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