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Keywords
(9)
Behavior Change
Change Point
Change Point Detection
Curve Fitting
Data Mining
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Iterative Algorithm
Time Series Data
Visual Inspection
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Event detection from time series data
Event detection from time series data,10.1145/312129.312190,Valery Guralnik,Jaideep Srivastava
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Event detection from time series data
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Citations: 158
)
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Valery Guralnik
,
Jaideep Srivastava
In the past few years there has been increased interest in using datamining techniques to extract interesting patterns from
time series data
generated by sensors monitoring temporally varying phenomenon. Most work has assumed that raw data is somehow processed to generate a sequence of events, which is then mined for interesting episodes. In some cases the rule for determining when a sensor reading should generate an event is well known. However, if the phenomenon is illunderstood, stating such a rule is difficult. Detection of events in such an environment is the focus of this paper. Consider a dynamic phenomenon whose behavior changes enough over time to be considered a qualitatively significant change. The problem we investigate is of identifying the time points at which the
behavior change
occurs. In the statistics literature this has been called the changepoint detection problem. The standard approach has been to (a) upriori determine the number of changepoints that are to be discovered, and (b) decide the function that will be used for
curve fitting
in the interval between successive changepoints. In this paper we generalize along both these dimensions. We propose an
iterative algorithm
that fits a model to a time segment, and uses a likelihood criterion to determine if the segment should be partitioned further, i.e. if it contains a new change point. In this paper we present algorithms for both the batch and incremental versions of the problem, and evaluate their behavior with synthetic and real data. Finally, we present initial results comparing the changepoints detected by the batch algorithm with those detected by people using visual inspection.
Conference:
Knowledge Discovery and Data Mining  KDD
, pp. 3342, 1999
DOI:
10.1145/312129.312190
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Citation Context
(105)
...Guralnik and Srivastava [
80
] propose an iterative algorithm and use a likelihood criterion to segment a time series into piecewise homogeneous regions to detect those change points, which are equivalent to the events defined by the change points, and to evaluate the change points within highway traffic data...
Huiqi Zhang
,
et al.
Socioscope: Human Relationship and Behavior Analysis in Social Network...
...Change point detection addresses the discovery of time points at which the behavior of time series data changes [
12
], [33], [39]...
Lei Shi
,
et al.
Anomalous Window Discovery for Linear Intersecting Paths
...In [
4
] a maximum likelihood estimator is used to fit a sequence of timeindexed models to raw data...
Bruno Nery
,
et al.
A dynamical systems approach to online event segmentation in cognitive...
...The first problem has been addressed in the works like [9], while the second is usually dealt with in great detail in the branch of statistics known as changepoint detection [10,
11
,12]...
Gennady L. Andrienko
,
et al.
Extracting Events from Spatial Time Series
...In the first stage, the local detector (LD) that are deployed on each monitored subnet performs changepoint detection [
3
, 4, 5, 6, 7] and tries to detect the events as a changepoint in a target metric such as outgoing traffic rate...
Mari Nakagawa
,
et al.
Performance evaluation of multistage changepoint detection scheme wi...
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Discrete wavelet transformbased time series analysis and mining
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, 2010