Welcome to the SAX (Symbolic Aggregate approXimation) Homepage!
SAX is the first symbolic
representation for time series that allows for dimensionality reduction
and indexing with a lowerbounding distance measure. In classic data
mining tasks such as clustering, classification, index, etc., SAX is as
good as wellknown representations such as Discrete Wavelet Transform
(DWT) and Discrete Fourier Transform (DFT), while requiring less
storage space. In addition, the representation allows researchers to
avail of the wealth of data structures and algorithms in bioinformatics
or text mining, and also provides solutions to many challenges
associated with current data mining tasks. One example is motif
discovery, a problem which we defined for time series data. There is
great potential for extending and applying the discrete representation
on a wide class of data mining tasks.
SAX was invented by Eamonn Keogh and Jessica Lin in 2002, using funding from NSF Career Award 0237918. Edward Tufte was kind enough to mention that SAX allows a sparkline like visualization of data. The relevant paper is this one [pdf]. Li Wei has generalized the SAX code to handle the N/n not equal an integer case, and to allow alphabet sizes up to 20. Download this zip file for the code and details. If you want a copy of my SAX time series/Shape tutorial, download this. Here is a video of Dr. Keogh giving a talk at Google about using SAX for various problems, including shape mining. Much of the utility of SAX has now been subsumed by iSAX , which is a generalization of SAX that allows indexing and mining of massive datasets. Visit the iSAX page. Try this Matlab code snippet: startRange = 2; stdc= 1; endRange = 512; table = cell(endRangestartRange,1); for r=startRange:endRange table{rstartRange+1} = norminv((1:r1)/r,0,stdc); end This will generate a table of breakpoints from 2 to 512, using std 1. 

Papers by Keogh and collaborators that use SAX. (in random order)
In [1] we show how to use SAX to find time series discords which are unusual time series. In [2] we consider a special case of SAX, which has an alphabet size of 2, and a word size equal to the raw data, and show that we can use this bitlevel representation for a variety of data mining tasks. In [3] we show how to use SAX to create time series bitmaps, which allow visualization of time series data directly within a standard GUI such as MS Windows. In [4] we further show how to use time series bitmaps to do anomaly detection. In [5] we show that SAX can support parameterlite data mining of time series, including classification and clustering. In [7] we show that SAX can replace standard representations of time series (i.e DWT, DFT) for all classic data mining problems including classification, clustering and indexing. We first used SAX to find time series motifs (exactly, and somewhat fast) in [9], and later showed a blinding fast probabilistic algorithm in [8]. In [10] we tentatively showed how to use SAX to meaningfully cluster time series streams. In [12] we show an application of SAX to a shape mining problem, and in [11] we generalize the time series bitmap concept to more general datasets. In [13] we show how to use SAX to find approximately duplicated shapes (shape motifs) in large databases. Paper [14] is a journal paper reviewing SAX first two years. Paper [15] shows how to find motifs under uniform scaling. Paper [16] introduces iSAX. Paper [17] shows how to do SAX on resource limited sensors.
E. Keogh, J. Lin and A. Fu (2005). HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In Proc. of the 5th IEEE International Conference on Data Mining (ICDM 2005), pp. 226  233., Houston, Texas, Nov 2730, 2005. [pdf ]. More info on discords and HOT SAX is here Also KAIS journal paper.
Ratanamahatana, C., Keogh, E., Bagnall, T. and Lonardi, S. (2005). A Novel Bit Level Time Series Representation with Implications for Similarity Search and Clustering. PAKDD 05. [pdf ] Also DMKD journal paper.
Kumar, N., Lolla N., Keogh, E., Lonardi, S. , Ratanamahatana, C. A. and Wei, L. (2005).
Timeseries Bitmaps: A Practical Visualization Tool
for working with Large Time Series Databases
Li Wei, Nitin Kumar, Venkata Nishanth Lolla, Eamonn Keogh, Stefano Lonardi, Chotirat Ann Ratanamahatana (2005). AssumptionFree Anomaly Detection in Time Series. In Proc. of the 17th International Scientific and Statistical Database Management Conference (SSDBM 2005), Santa Barbara, CA, U.S.A., June 2729, 2005.
Keogh, E., Lonardi, S. and Ratanamahatana, C. (2004). Towards ParameterFree Data Mining. In proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA, Aug 2225, 2004. [pdf, slides ] Also DMKD journal paper.
Lin, J., Keogh, E., Lonardi, S., Lankford, J. P. & Nystrom, D. M. (2004). Visually Mining and Monitoring Massive Time Series. In proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, WA, Aug 2225, 2004. [pdf ,slides] Also Information Visualization journal paper.
Lin, J., Keogh, E., Lonardi, S. & Chiu, B. (2003) A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery. San Diego, CA. June 13. [pdf, slides]
Chiu, B. Keogh, E., & Lonardi, S. (2003). Probabilistic Discovery of Time Series Motifs. In the 9^{th} ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 24  27, 2003. Washington, DC, USA. pp 493498. [Expanded Version pdf]
Patel, P., Keogh, E., Lin, J., & Lonardi, S. (2002). Mining Motifs in Massive Time Series Databases. In proceedings of the 2002 IEEE International Conference on Data Mining. Maebashi City, Japan. Dec 912.
Keogh, J. Lin, and W. Truppel. (2003). Clustering of Time Series Subsequences is Meaningless: Implications for Past and Future Research. In proceedings of the 3rd IEEE International Conference on Data Mining . Melbourne, FL. Nov 1922. pp 115122. [ pdf] Also KAIS journal paper.
Eamonn Keogh, Li Wei, Xiaopeng Xi, Stefano Lonardi, Jin Shieh, Scott Sirowy (2006). Intelligent Icons: Integrating LiteWeight Data Mining and Visualization into GUI Operating Systems. ICDM 2006. [pdf]
Li Wei, Eamonn Keogh and Xiaopeng Xi (2006) SAXually Explict Images: Finding Unusual Shapes. ICDM 2006. [pdf]. Now a Data Mining and Knowledge Discovery Journal paper.
Xiaopeng Xi, Eamonn Keogh, Li Wei, Agenor MafraNeto (2007). Finding Motifs in a Database of Shapes. SIAM International Conference on Data Mining.
Jessica Lin, Eamonn Keogh Li Wei and Stefano Lonardi (2007) Experiencing SAX: a Novel Symbolic Representation of Time Series. DMKD Journal.
Dragomir Yankov, Eamonn Keogh, Jose Medina, Bill Chiu, and Victor Zordan (2007). Detecting Motifs Under Uniform Scaling. SIGKDD 2007. [pdf] Supporting webpage with video and datasets.
Jin Shieh and Eamonn Keogh (2008). iSAX: Indexing and Mining Terabyte Sized Time Series. SIGKDD 2008. Also DMKD journal paper.
Shashwati Kasetty, Candice Stafford, Gregory P. Walker, Xiaoyue Wang, Eamonn Keogh (2008). RealTime Classification of Streaming Sensor Data. 20th IEEE Int'l Conference on Tools with Artificial Intelligence. [pdf]
Alessandro Camerra, Themis Palpanas, Jin Shieh and Eamonn Keogh (2010) iSAX 2.0: Indexing and Mining One Billion Time Series. ICDM 2010 [pdf]
Selected papers by others that use SAX.
In [A] the authors "New approaches for representing, analyzing and visualizing complex kinetic mechanisms", they note "The procedure is based on the methodology recently proposed by (Lin and Keogh) for the analysis of multidimensional time series". Papers [B.C,D,E] use SAX and random projection (see [8] above) to discover motifs in telemedicine time series. In paper [F] the authors convert plamprint to time series, then to SAX, then they do biometric recognition. Paper [G] says "we take Motif developed by Keogh in order to support a medical expert in discovering interesting knowledge". Paper [H] uses SAX and random projection (see [8] above) to mine motion capture data. Paper [I] uses SAX to find repeated patterns in motion capture data. Paper [J] uses SAX to find rules in time series. Paper [K] uses SAX to find motifs of unspecified length. Paper [L] uses SAX to find repeated patterns in robot sensors. Androulakis et. al. [M] uses SAX for electing Maximally Informative Genes to. Enable Temporal Expression Profiling. In paper [N] the authors us SAX to do Spatiotemporal Trajectory Joins. In [O] the authors use SAX motifs to "analyze respiration wave during cello performance" Paper [P] uses SAX to "detect multiheaded stealthy attack tools". Paper [Q] is using SAX to " Understand the formation of tornadoes"! Paper [R] uses SAX and time series motifs to for the Selection of Informative Genes in TimeCourse Gene Expression Data. Paper [S] makes the minor extensions to [8] above, to allow it to handle the multidimensional case. Ph.d Thesis [T] uses SAX for a variety of tasks in network traffic analysis. Paper [U] uses SAX to do Anomaly Detection in Network Traffic. Paper [V] uses SAX to do prediction of severe weather phenomena such as tornados, thunderstorms, hail, and floods. Paper [W] uses a modification of SAX to discover novel gene relations by mining similar subsequences in timeseries microarray data. Paper [X] uses SAX for classification of environmental sounds. [Y] uses SAX for financial data mining. Paper [Z] uses SAX for motif discovery. Paper [AA] uses SAX to find motifs in motion capture data. paper [AB] uses SAX based motifs to mine system call sequences. Paper [AB] uses SAX to classify control chart patterns. Papers [AD] and [AE] extend SAX for segmentation of time series into natural episodes. Paper [AF] uses SAX to find anomalies in SAX in a marine engine. Paper [AG] uses SAX for the selection of informative genes. paper [AH] uses SAX to detect complex events in wireless sensor networks. Paper [AI] uses SAX to mine MRIs. Paper [AJ] uses SAX to mine motion capture data. Paper [AK] uses SAX for privacypreserving discovery of frequent patterns in time series. Paper [AL] uses SAX to find association rules in time series. Paper [AM] uses SAX and Vistree to find patterns in CPU traces. Paper [AN] uses SAX for similarity search. Paper [AO] uses a SAXlike (but not SAX) approach for assessing the wellbeing of unsupervised, vulnerable individuals. Paper [AP] uses SAX for characterizing the mechanism of action of antiinflammatory drugs. Paper [AQ] uses SAX to visualize patterns that may differentiate between medical conditions such as renal and respiratory failure. Paper [AR] uses SAX to Understand malicious internet traffic by mining honeypot traces. Paper [AS] uses SAX to mine ECG data. Paper [AT] uses SAX to tokenize for gestures. Paper [AU] uses SAX for palm print biometrics. Paper [AV] uses SAX for ECG pattern recognition on mobile devices.Paper [AW] uses SAX for largescale network traffic analysis. Paper [AX] uses SAX for robotic motion segmentaion. Paper [AY] uses SAX for as part of a complexity measure for nonstationary signals. Paper [AZ] uses SAX for mining color distributions in images. Paper [BA] uses SAX for measuring brain states. Paper [BB] uses SAX for mining hurricane data. Paper [BC] uses SAX for quality control in semiconductor manufacturing. Paper [BD] uses SAX as an input to a Markov prediction system. Paper [BE] uses SAX for panic disorder treatment. Paper [BF] uses SAX for analogcircuit fault diagnosis using threestage preprocessing and time series data. Paper [BG] uses SAX for Mining closed flexible patterns in timeseries databases. Paper [BH] uses SAX for clustering industrial heating telemetry. Paper [BI] uses SAX for a computational resource advisory system. Paper [BJ] uses SAX to mine human gait data. Paper [BK] (in Portuguese) uses SAX to mine river levels. Paper [BL] augments SAX for the identification of informative genes in replicated microarray experiments. Paper [BM] uses SAX (actually iSAX) for mining motifs. Paper [BN] uses SAX for shape mining. ...and so on...
Androulakis, I. P. (2005). New Approaches for Representing, Analyzing and Visualizing Complex Kinetic Mechanisms. . In proceedings of the 15th European Symposium on Computer Aided Process Engineering. Barcelona, Spain. May 29June 1.
Silvent, A., Dojat, M. & Garbay, C. (2004). Multilevel Temporal Abstraction for Medical Scenario Construction. International Journal of Adaptive Control and Signal Processing.
Silvent, A. S., Carbay, C., Carry, P. Y. & Dojat, M. (2003). Data, Information and Knowledge for Medical Scenario Construction. In proceedings of the Intelligent Data Analysis In Medicine and Pharmacology Workshop (IDAMAP 2003). October. Protaras, Cyprus.
F. Duchene, C. Garbay, V. Rialle, "Mining heterogeneous multivariate timeseries for learning meaningful patterns: Application to home health telecare," Research Report 1070I, Institut d Informatique et Mathematiques Appliquees de Grenoble (IMAG), Grenoble, France, 2004.
F. Duchene and C. Garbay, Apprentissage de motifs temporels, multidimensionnels et heterogenes  Application a la telesurveillance medicale, Conference francophone sur lapprentissage automatique (CAP), Nice, France, 31 mai  3 juin 2005. Presses Universitaires de Grenoble.
Chen, J. S., Moon, Y. S. & Yeung, H. W. (2005). Palmprint Authentication Using Time Series. In proceedings of the 5th International Conference on Audio and VideoBased Biometric Person Authentication. Hilton Rye Town, NY. July 2022.
Kitaguchi, S. (2004). Extracting Feature based on Motif from a Chronic Hepatitis Dataset. In proceedings of the 18th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI). Kanazawa, Japan. June 24.
Tanaka, Y. & Uehara, K. (2004). Motif Discovery Algorithm from Motion Data. In proceedings of the 18th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI). Kanazawa, Japan. June 24.
Celly, B. & Zordan, V. B. (2004). Animated People Textures. In proceedings of the 17th International Conference on Computer Animation and Social Agents (CASA 2004). July 79. Geneva, Switzerland.
Ohsaki, M., Sato, Y., Yokoi, H. & Yamaguchi, T. (2003). A Rule Discovery Support System for Sequential Medical Data In the Case Study of a Chronic Hepatitis Dataset. ECML 2003
Tanaka, Y. & Uehara, K. (2003). Discover Motifs in Multi Dimensional TimeSeries Using the Principal Component Analysis and the MDL Principle. In proceedings of the 3rd International Conference on Machine Learning and Data Mining in Pattern Recognition. pp.252265.
Koji Murakami Yoshikazu Yano Shinji Doki Shigeru Okuma (2004). Behavior extraction from a series of observed robot motion . RoboMec2004
Androulakis, I.P., J. Wu, J. Vitolo and C. Roth, Selecting maximally informative genes to enable temporal expression profiling analysis, Proceedings of Foundations of Systems Biology in Engineering, Santa Barbara, CA, (2005)
P. Bakalov, M. Hadjieleftheriou, V. J. Tsotras, (2005). Time Relaxed Spatiotemporal Trajectory Proc. of the ACM International Symposium on Advances in Geographic Information Systems(ACMGIS),Bremen, Germany, November 2005.
Keita Kinjo Tomonobu Ozaki Keigo Sawai Koichi Furukawa (2005) Knowledge acquisition from time series data by association rule and network analysis. The 19th Annual Conference of the Japanese Society for Artificial Intelligence, 2005
F. Pouget, G. UrvoyKeller, and M. Dacier Time Signatures to detect multiheaded stealthy attack tools In 18th Annual FIRST Conference Baltimore, Maryland, USA June 2006.
Amy McGovern, Univ. of Oklahoma, Norman, OK; and A. Kruger, D. Rosendahl, and K. Droegemeier. (2007) Understanding the formation of tornadoes through data mining. Fifth Conference on Artificial Intelligence Applications to Environmental Science.
Eric Yang and Ioannis Androulakis (2006) Selection of Informative Genes in TimeCourse Gene Expression Data. AIChE 2006.
David Minnen, Thad Starner, Irfan Essa, and Charles Isbell. Improving Activity Discovery with Automatic Neighborhood Estimation. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), 2007.
Fabian Pouget (2005). Distributed System of Honeypot Sensors. Telecom Paris
Masayuki Okabe, Taeko Miwa, Kyoji Umemura (2006) Anomaly Detection in Network Traffic based on String Analysis. IC2006.
McGovern, Amy, and Rosendahl, Derek H., and Kruger, Adrianna, and Beaton, Meredith G., and Brown, Rodger A., and Droegemeier, Kelvin K. (2007) Understanding the formation of tornadoes through data mining. Fifth Conference on Artificial Intelligence and its Applications to Environmental Sciences at the American Meteorological Society annual conference.
Vincent ShinMu Tseng, L. C. Chen and J. J. Liu (2006) Discovering Novel Gene Relations by Mining Similar Subsequences in Time in TimeSeries Microarray Data. in Proc. Intl Workshop on Science of Artificial, Taiwan, 2005
Automated Ensemble Extraction and Analysis of Acoustic Data Streams. Technical Report MSUCSE0640. December 2006. Eric P. Kasten and Philip K. McKinley Stuart H. Gage
Application Research of a New Symbolic Approximation MethodSAX in Time Series Mining (2006) LIU Yi,BAO Depei,YANG Zehong. COMPUTER ENGINEERING AND APPLICATIONS 2006 Vol.42 No.27
Motif Detection Inspired by Immune Memory (2007) William Wilson, Phil Birkin, and Uwe Aickelin
Motion motif extraction from highdimensional motion information. Araki , Arita and Taniguchi 2006
Wilson Will, Feyereisl Jan and Aickelin Uwe (2007): Detecting Motifs in System Call Sequences, Proceedings of the 8th International Workshop on Information Security Applications (WISA 2007), Lecture Notes in Computer Science, pp, Jeju, Korea
K. Lavangnananda, and C. Wongwattanakarn (2007) Utilizing Symbolic Representation and Evolutionary Computation in Classification of Control Chart Patterns. Soft Computing in Industrial Applications 2007
T. Armstrong and T .Oates. RIPTIDE: Segmenting Data Using Multiple Resolutions. In the Proceedings of the 6th IEEE International Conference on Development and Learning (ICDL), 2007.
T. Armstrong and T. Oates. UNDERTOW: MultiLevel Segmentation of RealValued Time Series. In the Proceedings of the 22nd AAAI Conference on Artificial Intelligence (AAAI) (student abstract), 2007.
Time Discretisation Applied to Anomaly Detection in a Marine Engine. Morgan, Liu, Turnbull, and Brown 2007.
Yang, E., F. Berthiamume, M. L. Yarmush, and I. P. Androulakis. SeLection of INformative Genes via Symbolic Hashing Of Time Series .Proceedings of the Joint 9th International Symposium, Processing Systems Engineering and 16th European Symposium, 2006.
M. Zoumboulakis and G. Roussos, Escalation: Complex Event Detection in Wireless Sensor Networks ,in Proceedings of 2nd European Conference on Smart Sensing and Context (EuroSSC), 2325 Oct 2007, Lake District, UK.
M. Fabri, G. Mascioli, G. Palonara, A. M. Perdon, S. R. Viola (2007) Activation and delay in FMRI brain signals of selective attention. in Proceedings of Int. IJCNN07 Workshop on Neurodynamics, Orlando, Florida, USA, August 17, 2007.
Kosuke Makio, Yoshiki Tanaka, and Kuniaki Uehara (2007) Discovery of Skills from Motion Data. New Frontiers in Artificial Intelligence
Da Silva, J.C.; Klusch, M. (2007): PrivacyPreserving Discovery of Frequent Patterns in Time Series. Proceedings of the 7th Industrial Conference on Data Mining ICDM, Leipzig, Germany, Springer.
Discovery Association Rules in Time Series Data. Kittipong Warasup and Chakarida Nukoolkit
Ooi Boon Yaik Chan Huah Yong Fazilah Haron (2006) CPU Usage Pattern Discovery Using Suffix Tree. Distributed Frameworks for Multimedia Applications, 2006.
Combining SAX and Piecewise Linear Approximation to Improve Similarity Search on Financial Time Series. Hung, Nguyen Quoc Viet Anh, Duong Tuan Information Technology Convergence, 2007. ISITC 2007.
Julia Hunter and Martin Colley (2007) Feature Extraction from Sensor Data Streams for RealTime Human Behaviour Recognition. PKDD2007
Analysis of Regulatory Interaction Networks from Clusters of Coexpressed Genes (2008) E. Yang et al
Visualizing Multivariate Time Series Data to Detect Specific Medical Conditions. Ordonez et al. AMIA 2008.
Almotairi, Saleh I. et al (2007) Extracting Interarrival Time Based Behaviour from Honeypot Traffic using Cliques.
Kulahcioglu B., Ozdemir S., Kumova B.I., Application of Symbolic Piecewise Aggregate Approximation (PAA) Analysis to ECG Signals, The 17th IASTED International Conference on Applied Simulation and Modelling (ASM 2008) .
Tokenization for Gesture Space Modelling. Aaron Licata, Alexandra Psarrou 13th International Conference on Applications of Natural Language to Information Systems, Doctoral Symposium (NLDB'08DS)
Using SIFT Features in Palmprint Authentication. Jiansheng Chen YiuSang Moon. ICPR'08
Eirik Aanonsen and Rune Fensli, Pattern recognition on mobile devices. 2006
Applying multiple time series data mining to largescale network traffic analysis Weisong He,; Guangmin Hu,; Xingmiao Yao,; Guangyuan Kan,; Hong Wang,; Hongmei Xiang 2008.
Motion segmentation for humanoid control planning. Matthew Field, David Stirling , Fazel Naghdy, Zengxi Pan. ACRA 2008
Erik M. Bollt, Joseph D. Skufca, Stephen J McGregor, Control Entropy: A Complexity Measure for Nonstationary Signals, Mathematical Biosciences and Engineering, 6 1 125 (2009)
Effective Image Mining by Representing Color Histograms as Time Series (2007) Zaher Al Aghbari. Journal of Advanced Computational Intelligence and Intelligent Informatics
Combining Electroencephalograph and Functional Near Infrared Spectroscopy to Explore Users' Mental Workload. Leanne M. Hirshfield. 2009.
Range Queries over Trajectory Data with Recursive Lists of Clusters: a case study with Hurricanes data. GISRUK 2009
Fullline FDC Diagnosis System via Signals Similarity Measure. TzuCheng Lin and ShirKuan Lin. AEC/APC Symposium Asia 2009
Predicting Future States with nDimensional Markov Chains for Fault Diagnosis. Morgan and Lui, IEEE Transactions on Industrial Electronics.
Intelligent panic disorder treatment by using biofeedback analysis and web technologies. Shie et al International Journal of Business Intelligence and Data Mining 5(1) 2010
AnalogCircuit Fault Diagnosis Using ThreeStage Preprocessing and Time Series Data. Mining. Weisong He, Hongmei Xiang and Jingyuan 2009
Mining closed flexible patterns in timeseries databases . HueiWen Wu, Anthony J.T. Lee 2010
Clustering Techniques Applied to MultipleModels Structures. Silva , Becerra & Calado 2009
CPU Usage Pattern Discovery Using Suffix Tree For Computational Resource Advisory System . Ooi, Boon Yaik (2006)
Human Gait Data Mining by Symbol Based Descriptive Features. 2009. Ergovic, Tonkovic, and Medved
Analise de padroes sequenciais em serie historica do rio Paraguai. Anais 2 Simposio de Geotecnologias no Pantanal, Corumba, 711 novembro 2009, p.323332. Laurimar Goncalves Vendrusculo, Stanley Robson de Medeiros Oliveira, Julio Cesar Dalla Mora Esquerdo, Joao Francisco Goncalves Antunes
A New Symbolic Representation for the Identification of Informative Genes in Replicated Microarray Experiments. (2010) Jeremy D. Scheff, Richard R. Almon, Debra C. DuBois, William J. Jusko, and Ioannis P. Androulakis
Multiresolution Motif Discovery in Time Series. Nuno C. Castro and Paulo Azevedo, SDM 2010
Relevant shape contour snippet extraction with metadata supported hidden Markov models. Wang and Candan. CIVR 10.
Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study. Santanu Das, Bryan Matthews, Ashok Srivastava, ; Nikunj Oza, NASA Ames Research Center (For the continuous data, each time series was SAX transformed)
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is? Indre Zliobaite (we used quadruple SAX representation of the series..)
A 3D Visualization Technique for Large Scale TimeVarying Data. Maiko Imoto, Takayuki Ito (We apply SAX for symbolic character representation of timevarying values )
An Animated Multivariate Visualization for Physiological and Clinical Data in the ICU. IHI 10 . Ordonez et al.
Michael Zoumboulakis, George Roussos: Complex Event Detection in Extremely ResourceConstrained Wireless Sensor Networks. MONET 16(2): 194213 (2011)
RASAX: ResourceAware Symbolic Aggregate approXimation for Mobile ECG Analysis, Hossein Tayebi , Shonali Krishnaswamy ,Agustinus Waluyo, Abhijat Sinha , , Mohamed Gaber .
Can Temporal and Spatial Patterns of Dynamc Terrain State Properties be Determined Using a Symbolic Aggregate ApproXimation (SAX) Approach? FRANKENSTEIN ICMG 2011
Event Detection using Archived Smart House Sensor Data obtained using Symbolic Aggregate Approximation. A. Onishi and C. Watanabe. PDPTA 2011.
3D TimeVarying Data Visualization Method Technique Featuring Symbolic Aggregate approximation ,M. Imoto, T. Itoh, IEEE Pacific Visualization 2011.
Legato and Glissando identification in Classical Guitar. Ozaslan and Arcos 2010. 7th Sound and Music Computing Conference
Attack Based Articulation Analysis of Nylon String Guitar. Ozaslan and Arcos CMMR2010
Voigtmann, C.; Lau, S. L. & David, K. (2011), An Approach to Collaborative Context Prediction, in '2011 IEEE International Conference on Pervasive Computing and Communications Workshops. IEEE
Improving the Classification Accuracy of Streaming Data Using SAX Similarity Features. Pekka Siirtola et al Pattern Recognition Letters.
Discovering Patterns for Prognostics: A Case Study in Prognostics of Train Wheels. Chunsheng Yang and Sylvain Letournea. IEA/AIE (1) 2011: 165175
MAGIC 2.0: A Web Tool for False Positive Prediction and Prevention for Gesture Recognition Systems. Daniel Kohlsdorf, Thad Starner, Daniel Ashbrook: FG' 11, 2011
Activity Recognition with Finite State Machines. Wesley Kerr, Anh Tran and Paul Cohen IJCAI 2011
Rana D. Parshad, Stephen J. McGregor, Michael A. Busa, Joseph D. Skufca, Erik Bollt A statistical approach to the use of Control Entropy identifies differences in constraints of gait in highly trained versus untrained runners, Mathematical Sciences and Engineering (MBE) (2011)
Heeyoul Choi, Chen Yu, Olaf Sporns and Linda Smith, "From Data Streams to Information Flow: Information Exchange in ChildParent Interaction," The Annual Meeting of the Cognitive Science Society (CogSci 2011), Boston, MA. July 2023, 2011.
Unsupervised Discovery of Motifs under Amplitude Scaling and Shifting in Time Series Databases. Tom Armstrong and Eric Drewniak.Lecture Notes in Computer Science, 2011, Volume 6871, Machine Learning and Data Mining in Pattern Recognition, Pages 539552
Pekka Siirtola, Heli Koskimäki, Ville Huikari, Perttu Laurinen, Juha Röning: Improving the classification accuracy of streaming data using SAX similarity features. Pattern Recognition Letters 32(13): 16591668 (2011)
F. Ciompi, O. Pujol, S. Balocco et al., “Automatic Key Frames Detection in Intravascular Ultrasound Sequences,” in MICCAI Workshop in Computing and Visualization for (Intra)Vascular Imaging (CVII), 2011
Takuma Nishii, Tomoyuki Hiroyasu, Masato Yoshimi, Mitsunori Miki, Hisatake Yokouchi: Similar subsequence retrieval from two time series data using homology search. SMC 2010: 10621067
Anita Sant’Anna et al (2011). A New Measure of Movement Symmetry in Early Parkinson’s Disease Patients Using Symbolic Processing of Inertial Sensor Data. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
Hyokyeong Lee, Asher MoodyDavis, Utsab Saha, Brian M. Suzuki, Daniel Asarnaw, Steven Chen, Michelle Arkin, Conor R. Caffrey, and Rahul Singh, Quantification and Clustering of Phenotypic Screening Data using TimeSeries Analysis for Chemotherapy of Schistosomiasis, BMC Genomics,