Finding surprising patterns in a time series database in linear time and space

By Eamonn J. Keogh, Stefano Lonardi, Bill Yuan-chi Chiu. Knowledge Discovery and Data Mining, 2002

480 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9891d654cd4fd53086e9ec939cc38c27a0fc1ffe

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In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Large-Scale Unusual Time Series Detection (2015, methodology): “We propose using Principal Component Analysis (PCA) together with multi-dimensional anomaly detection to identify unusual time series in a large collections of time series.”
A review on time series data mining (2011, methodology): “It is a non-trivial task to discover interesting patterns, which include frequently appearing (Fu et al., 2001) and surprising patterns (Keogh et al., 2002b), from time series data.”
A Review on Outlier/Anomaly Detection in Time Series Data (2020, methodology): “A paradigmatic example can be found in Keogh et al. [2002].”
Anomaly detection: A survey (2009, methodology): “The data in this domain typically has a temporal aspect and time-series analysis is also used in some techniques [Keogh et al. 2002, 2006; Basu and Meckesheimer 2007].”
Mining frequent spatio-temporal sequential patterns (2005, methodology): “The way to group the subsequence features ([7]) may be effective for time-series with 1-dimension val ues.”
Anomaly Detection Using Real-Valued Negative Selection (2003, methodology): “Data set and preprocessing The Mackey-Glass series [30] has been used as a test set for different anomaly detection approaches [5, 9, 24].”
Outlier Analysis (2013, methodology): “A method called TARZAN [257] uses suffix tree representations to efficiently determine all the anomalous subsequences in a comparative sense between a test sequence and a training sequence.”
Anomaly Detection for Discrete Sequences: A Survey (2012, methodology): “Keogh et al. [77] define a pattern to be surprising “ if the frequency of the pattern differs substantially from that expected by chance, given some previously seen data.””
A symbolic representation of time series, with implications for streaming algorithms (2003, methodology): “A Symbolic Representation of Time Series, with Implications for Streaming Algorithms Jessica Lin Eamonn Keogh Stefano Lonardi Bill Chiu University of California -Riverside Computer Science & Engineering Department Riverside, CA 92521, USA {jessica, eamonn, stelo, bill}@cs.ucr.edu ABSTRACT The…”
Spatio-Temporal Data Mining (2017, methodology): “This includes approaches for univariate time series [Bishop 1994; Keogh et al. 2005, 2002; Wei et al. 2005] as well as multivariate time series [Baragona and Battaglia 2007; Chen et al. 2008; Galeano et al. 2006; Takeishi and Yairi 2014] A survey of anomaly detection approaches for temporal data is…”
Gorilla: A Fast, Scalable, In-Memory Time Series Database (2015, methodology): “We found that this approach gives satisfactory answers to our question and was simpler to implement than similarly focused approaches described in the literature[10, 18, 16].”
Experiencing SAX: a novel symbolic representation of time series (2007, methodology): “…on the classic data mining tasks of clustering (Kalpakis et al. 2001), classiﬁcation (Geurts 2001), indexing (Agrawal et al. 1995; Faloutsos et al. 1994; Keogh et al. 2001a; Yi and Faloutsos 2000), and anomaly detection (Dasgupta and Forrest 1999; Keogh et al. 2002; Shahabi et al. 2000).”
Anomaly Detection in Time Series: A Comprehensive Evaluation (2022, unknown): “…NorM[14] DissimilarityAlgo[6] STOMP [164] Series2Graph [16] HOTSAX [70] STAMP [156] MERLIN[97] DAD[154] BoehmerGraph[13] GrammarViz [120] TARZAN [71] NormA-SJ [15] NormA-smpl[15] KnorrSeq2[102] TSBitmap [144] ILOF[108] VALMOD [82] SurpriseEncoding[26] EnsembleGI [43] DADS[119] PST [128]…”
Efficient elastic burst detection in data streams (2003, background): “In very recent work, Keogh et al.[15] propose an algorithm based on suffix tree structures to find surprising patterns in time series database.”
Time-series clustering - A decade review (2015, methodology): “Briefly, finding the clusters of time-series can be advantageous in different domains to answer following real world problems: Anomaly, novelty or discord detection: Anomaly detection are methods to discover unusual and unexpected patterns which happen in datasets surprisingly [31–34].”
Outlier Detection for Temporal Data: A Survey (2014, methodology): “To make the computations efﬁcient, the TARZAN algorithm was proposed which exploits suf-ﬁx trees [68], [70], [71].”