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1,171
UNUSUAL SUB-SEQUENCE IDENTIFICATIONS IN TIME SERIES WITH PERIODICITY
, 2005
"... Abstract. Fast and intelligent data mining has recently become an integral part of data analysis and a pre-requisite for modeling. This is largely due to the introduction of more sophisticated data collection tools and the possibility of observing large datasets at increased higher frequencies. This ..."
Abstract
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. This paper aims to investigate the current methodologies used for the detection of time series discord sub-sequences and especially those with periodicity. A strategy will be suggested to use classical data mining techniques and statistical decision making to take advantage of the special features
HMM-Based Defect Localization in Wire Ropes – A New Approach to Unusual Subsequence Recognition
- In Proceedings of the 31st Annual Pattern Recognition Symposium of the German Association for Pattern Recognition (DAGM 2009
, 2009
"... Abstract. Automatic visual inspection has become an important appli-cation of pattern recognition, as it supports the human in this demanding and often dangerous work. Nevertheless, often missing abnormal or de-fective samples prohibit a supervised learning of defect models. For this reason, techniq ..."
Abstract
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Cited by 6 (6 self)
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, techniques known as one-class classification and novelty- or un-usual event detection have arisen in the past years. This paper presents a new strategy to employ Hidden Markov models for defect localization in wire ropes. It is shown, that the Viterbi scores can be used as indicator for unusual subsequences
Hot sax: Efficiently finding the most unusual time series subsequence
, 2005
"... In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. Ti ..."
Abstract
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Cited by 108 (5 self)
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In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series
HOT SAX: Finding the Most Unusual Time Series Subsequence: Algorithms and Applications
"... In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. Ti ..."
Abstract
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Cited by 8 (0 self)
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In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series
On-line New Event Detection and Tracking
, 1998
"... We define and describe the related problems of new event detection and event tracking within a stream of broadcast news stories. We focus on a strict on-line setting-i.e., the system must make decisions about one story before looking at any subsequent stories. Our approach to detection uses a singl ..."
Abstract
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Cited by 206 (5 self)
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We define and describe the related problems of new event detection and event tracking within a stream of broadcast news stories. We focus on a strict on-line setting-i.e., the system must make decisions about one story before looking at any subsequent stories. Our approach to detection uses a
A Linear Time Algorithm for Finding All Maximal Scoring Subsequences
- In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
, 1999
"... Given a sequence of real numbers ("scores"), we present a practical linear time algorithm to find those nonoverlapping, contiguoussubsequenceshaving greatest total scores. This improves on the best previously known algorithm, which requires quadratic time in the worst case. The problem ..."
Abstract
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Cited by 62 (3 self)
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. The problem arises in biological sequence analysis, where the highscoring subsequences correspond to regions of unusual composition in a nucleic acid or protein sequence. For instance, Altschul, Karlin, and others have used this approach to identify transmembrane regions, DNA binding domains
Approximations to magic: Finding unusual medical time series
- In 18th IEEE Symp. on Computer-Based Medical Systems (CBMS
, 2005
"... In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While ..."
Abstract
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Cited by 20 (1 self)
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In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series
The concept of tourist area cycle of evolution: Implications for management of resources.
- Canadian Geographer.
, 1980
"... The concept of a recognizable cycle in the evolution of tourist areas is presented, using a basics curve to illustrate their waving and waning popularity. Specific stages in the evolutionary sequence are described, along with a range of possible future trends. The implications of using this model i ..."
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Cited by 191 (0 self)
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by Christaller: The typical course of development has the following pattern. Painters search out untouched and unusual places to paint. Step by step the place develops as a so-called artist colony. Soon a cluster of poets follows, kindred to the painters: then cinema people, gourmets, and the jeunesse doree
Finding the unusual medical time series: Algorithms and applications
- IEEE Trans. on Information Technology
"... Abstract — In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time se ..."
Abstract
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Cited by 2 (0 self)
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Abstract — In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time
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