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HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence

E. Keogh, J. Lin, A. Fu
Fifth IEEE International Conference on Data Mining (ICDM'05)  
They thus capture the sense of the most unusual subsequence within a time series.  ...  In this work, we introduce the new problem of finding time series discords.  ...  In general, if there are truly unusual patterns in the time series, the HOT SAX is even faster.  ... 
doi:10.1109/icdm.2005.79 dblp:conf/icdm/KeoghLF05 fatcat:tkzamk4npbcr7ct4pslwmwgkdi

Group SAX: Extending the Notion of Contrast Sets to Time Series and Multimedia Data [chapter]

Jessica Lin, Eamonn Keogh
2006 Lecture Notes in Computer Science  
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In proceedings of the 5 th IEEE International Conference on Data Mining (ICDM). Nov 27-30. Houston, TX. 11.  ...  Approximations to Magic: Finding Unusual Medical Time Series. In proceedings of the 18 th International Symposium on Computer-Based Medical Systems. June 23-24. Dublin, Ireland. 12.  ... 
doi:10.1007/11871637_29 fatcat:hcfiafnbbbgtbprdkqwe6qx7e4

HOT aSAX: A Novel Adaptive Symbolic Representation for Time Series Discords Discovery [chapter]

Ninh D. Pham, Quang Loc Le, Tran Khanh Dang
2010 Lecture Notes in Computer Science  
The best known approach to our knowledge is HOT SAX technique based on the equiprobable distribution of SAX representations of time series.  ...  Our empirical experiments with real-world time series datasets confirm the theoretical analyses as well as the efficiency of our approach.  ...  HOT SAX for Finding Time Series Discords Keogh at al.  ... 
doi:10.1007/978-3-642-12145-6_12 fatcat:lshdaow725ff7mwgds5oookor4

Some Novel Heuristics for Finding the Most Unusual Time Series Subsequences [chapter]

Mai Thai Son, Duong Tuan Anh
2010 Studies in Computational Intelligence  
In this work, we introduce some novel heuristics which can enhance the efficiency of the Heuristic Discord Discovery (HDD) algorithm proposed by Keogh et al. for finding most unusual time series subsequences  ...  , called time series discords.  ...  However, all these works did not give a clear and workable definition for the "most unusual subsequences" in a time series.  ... 
doi:10.1007/978-3-642-12090-9_20 fatcat:4dw5xnkp2bcf5nfmy3uyomuyhq

From Cluster-Based Outlier Detection to Time Series Discord Discovery [chapter]

Nguyen Huy Kha, Duong Tuan Anh
2015 Lecture Notes in Computer Science  
The experimental results show that our approach is much more efficient than the HOTSAX algorithm in detecting time series discords while the anomalous patterns discovered by the two methods perfectly match  ...  In this approach, first, subsequence candidates are extracted from the time series using a segmentation method, then these candidates are transformed into the same length and are input for an appropriate  ...  Eamonn Keogh for his kindly providing all the test datasets used in this work.  ... 
doi:10.1007/978-3-319-25660-3_2 fatcat:7wckknixhvb5rfza5byhdfsf5u

Finding the most unusual time series subsequence: algorithms and applications

Eamonn Keogh, Jessica Lin, Sang-Hee Lee, Helga Van Herle
2006 Knowledge and Information Systems  
They thus capture the sense of the most unusual subsequence within a time series.  ...  In this work we introduce the new problem of finding time series discords.  ...  We thank the reviewers who made valuable comments and suggestions. Thanks also to Li Wei and Xiaopeng Xi for help with the image processing algorithms.  ... 
doi:10.1007/s10115-006-0034-6 fatcat:ghvvjhzqibaxhcqwbpgk6ex7mm

THAAD: Efficient Matching Queries under Temporal Abstraction for Anomaly Detection [article]

Roni Mateless, Michael Segal, Robert Moskovitch
2019 arXiv   pre-print
Then we identify unusual subsequences in the resulting sequence using dynamic data structure based on the geometric observations supporting polylogarithmic update and query times.  ...  Time-series data are represented by a sequence of symbolic time intervals, describing increasing and decreasing trends, in a compact way using gradient temporal abstraction technique.  ...  We obtain a better theoretical worst-case runtime solution to the HOT SAX [20] [21] approach which looks for the most unusual subsequence in a given sequence and have worst-case quadratic running time  ... 
arXiv:1911.00336v2 fatcat:knsyxoynxzgt3olvarva2xdwxy

THE EVOLUTION OF THERMOTOLERANCE IN HOT SPRING CYANOBACTERIA OF THE GENUS SYNECHOCOCCUS

S.R. Miller, R.W. Castenholz
2000 Journal of Phycology  
We hypothesize that a benefit of phagotrophy in G. galatheanum is the acquisition of precursor linolenic acid (18:3n-3) that fuels LC-PUFA synthesis.  ...  G. galatheanum , like many photosynthetic dinoflagellates, contains high amounts of n-3 long-chain-polyunsaturated fatty acids (LC-PUFA) such as docosahexaenoic acid (DHA, 22:6n-3) and the hemolytic toxic  ...  A comparison between experimental series of different duration revealed that the metal accumulation increases time-dependently for Mn and Co.  ... 
doi:10.1046/j.1529-8817.1999.00001-143.x fatcat:sj56brok75ahna2ik3jismvv2m

Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

Haemwaan Sivaraks, Chotirat Ann Ratanamahatana
2015 Computational and Mathematical Methods in Medicine  
In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs.  ...  Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate.  ...  The authors also appreciate the help from Sorrachai Yingchareonthawornchai, Michigan State University, for his constructive reviews and comments on the preview version of this work.  ... 
doi:10.1155/2015/453214 pmid:25688284 pmcid:PMC4320938 fatcat:seuo6r6bxrhh5agyqmf6eetoha

Root System Water Consumption Pattern Identification on Time Series Data

2017 Sensors  
This study uses time series analysis methods for outliers' detection and pattern recognition on soil moisture sensor data to identify irrigation and consumption patterns and to improve a soil moisture  ...  The best result is obtained by the Series Strings Comparison (SSC) algorithm averaging a precision of 0.872 on the testing sets, vastly improving the current system's 0.348 precision.  ...  The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  ... 
doi:10.3390/s17061410 pmid:28621739 pmcid:PMC5492835 fatcat:d62h4x242nci3c73hkvyih2kou

Local Recurrence Rates with Automatic Time Windows for Discord Search in Multivariate Time Series

Chao-Lung Yang, Frederik Darwin, Hendri Sutrisno
2019 Procedia Manufacturing  
By identifying anomalous subsequences of time series data collected from manufacturing machines, the issue of malfunction can be detected based on the evaluation of operating conditions.  ...  Second, the subsequence search is performed through automatic time window local search. The time window is adjusted to the characteristic of the element exists in the time window.  ...  Acknowledgements We appreciate the financial support from National Science Council of Taiwan  ... 
doi:10.1016/j.promfg.2020.01.261 fatcat:sfojyaws2zhchdnnrbbamy4xji

Event Discovery in Time Series [article]

Dan Preston, Pavlos Protopapas, Carla Brodley
2009 arXiv   pre-print
We apply our method to over 100,000 astronomical time series from the MACHO survey, in which 56 different sections of the sky are considered, each with one or more known events.  ...  The discovery of events in time series can have important implications, such as identifying microlensing events in astronomical surveys, or changes in a patient's electrocardiogram.  ...  Acknowledgments We would like to thank Ira Gessel from the Mathematics Department at Brandeis University for his help.  ... 
arXiv:0901.3329v1 fatcat:4rs3olig7be3lgfwm776w3gk7i

Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection [article]

Konstantinos Bountrogiannis, George Tzagkarakis, Panagiotis Tsakalides
2021 arXiv   pre-print
series data.  ...  Due to the importance of the lower bounding distances and the attractiveness of symbolic representations, the family of symbolic aggregate approximations (SAX) has been used extensively for encoding time  ...  The subsequence time series length N varies in {480, 960, 1440, 1920} and the number of bytes per subsequence varies in {8, 16, 24, 40}.  ... 
arXiv:2105.09592v1 fatcat:qm56srmayjcv3dpsuu6gd5cery

A review on outlier/anomaly detection in time series data [article]

Ane Blázquez-García, Angel Conde, Usue Mori, Jose A. Lozano
2020 arXiv   pre-print
This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series.  ...  Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series.  ...  The abovementioned discord detection techniques are limited to finding the most unusual subsequence within a time series.  ... 
arXiv:2002.04236v1 fatcat:uypjdi6uufajpi43774d5exq7e

Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment

Amir Talaei-Khoei, James M Wilson, Seyed-Farzan Kazemi
2019 JMIR Public Health and Surveillance  
Periodic pattern mining or periodicity detection is process of finding periodic patterns in time series database.  ...  The types of periodicities are symbol periodicity, sequence periodicity and segment periodicity and they should be identified even in the presence of noise in the time series database.  ...  Al., "Finding Maximal Periodic Patterns and Volume-1, Issue-5, June 2012. [16] Eamonn Keogh, Jessica Lin, Ada Fu "HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence", Proceedings of  ... 
doi:10.2196/11357 fatcat:b7jxylw54vaelhgn32bjvvx5ku
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