TY - GEN
T1 - Time series change detection using segmentation
T2 - 2012 Conference on Intelligent Data Understanding, CIDU 2012
AU - Mithal, Varun
AU - O'Connor, Zachary
AU - Steinhaeuser, Karsten
AU - Boriah, Shyam
AU - Kumar, Vipin
AU - Potter, Christopher S.
AU - Klooster, Steven A.
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Segmentation of a time series attempts to divide it into homogeneous subsequences, such that each of these segments are different from each other. A typical segmentation framework involves selecting a model that is used to represent the segment. In this paper, we investigate segmentation scores based on difference between models and propose two approaches for normalizing the difference based score. The first approach uses permutation testing to assign a p-value to model difference. The second approach builds on bootstrapping methodology used in statistics which estimates the null distribution of complex statistics whose standard errors are not analytically derivable by generating alternative versions of the data by a resampling strategy. More specifically, given a time series with either a single or two segments, we propose a method to estimate the distribution of model difference statistic for each segment. The proposed approach allows normalizing model difference statistic when complex models are being used in the segmentation algorithm. We study the strengths and weaknesses of the two normalizing approaches in the context of characteristics of land cover data such as seasonality and noise using synthetic and real data sets. We show that relative performance of normalization approaches can vary significantly depending on the characteristics of the data. We illustrate the utility of these approaches for detection of deforestation in Mato Grosso (Brazil).
AB - Segmentation of a time series attempts to divide it into homogeneous subsequences, such that each of these segments are different from each other. A typical segmentation framework involves selecting a model that is used to represent the segment. In this paper, we investigate segmentation scores based on difference between models and propose two approaches for normalizing the difference based score. The first approach uses permutation testing to assign a p-value to model difference. The second approach builds on bootstrapping methodology used in statistics which estimates the null distribution of complex statistics whose standard errors are not analytically derivable by generating alternative versions of the data by a resampling strategy. More specifically, given a time series with either a single or two segments, we propose a method to estimate the distribution of model difference statistic for each segment. The proposed approach allows normalizing model difference statistic when complex models are being used in the segmentation algorithm. We study the strengths and weaknesses of the two normalizing approaches in the context of characteristics of land cover data such as seasonality and noise using synthetic and real data sets. We show that relative performance of normalization approaches can vary significantly depending on the characteristics of the data. We illustrate the utility of these approaches for detection of deforestation in Mato Grosso (Brazil).
UR - http://www.scopus.com/inward/record.url?scp=84872409445&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872409445&partnerID=8YFLogxK
U2 - 10.1109/CIDU.2012.6382202
DO - 10.1109/CIDU.2012.6382202
M3 - Conference contribution
AN - SCOPUS:84872409445
SN - 9781467346252
T3 - Proceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012
SP - 63
EP - 70
BT - Proceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012
Y2 - 24 October 2012 through 26 October 2012
ER -