Domain agnostic online semantic segmentation for multi-dimensional time series
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Eamonn J. Keogh | Wei Ding | Shaghayegh Gharghabi | Chin-Chia Michael Yeh | Yifei Ding | Paul Hibbing | Samuel LaMunion | Andrew Kaplan | Scott E. Crouter | Samuel R LaMunion | S. Crouter | P. Hibbing | W. Ding | Yifei Ding | Andrew Kaplan | Shaghayegh Gharghabi
[1] Eamonn J. Keogh,et al. Semi-Supervision Dramatically Improves Time Series Clustering under Dynamic Time Warping , 2016, CIKM.
[2] Naonori Ueda,et al. Fast and Exact Monitoring of Co-Evolving Data Streams , 2014, 2014 IEEE International Conference on Data Mining.
[3] Dan Morris,et al. RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises , 2014, CHI.
[4] Haixun Wang,et al. Finding semantics in time series , 2011, SIGMOD '11.
[5] Jake K. Aggarwal,et al. Semantic labeling of track events using time series segmentation and shape analysis , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).
[6] Horst Bunke,et al. Off-Line, Handwritten Numeral Recognition by Perturbation Method , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[7] Christoph Bregler,et al. Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[8] Eamonn J. Keogh,et al. Classification of streaming time series under more realistic assumptions , 2015, Data Mining and Knowledge Discovery.
[9] J. Sallis,et al. Using accelerometers in youth physical activity studies: a review of methods. , 2013, Journal of physical activity & health.
[10] Norman I. Badler,et al. Semantic Segmentation of Motion Capture Using Laban Movement Analysis , 2007, IVA.
[11] C. Cassisi,et al. Probabilistic Reasoning Over Seismic Time Series: Volcano Monitoring by Hidden Markov Models at Mt. Etna , 2016, Pure and Applied Geophysics.
[12] Michelle Karg,et al. Movement Primitive Segmentation for Human Motion Modeling: A Framework for Analysis , 2016, IEEE Transactions on Human-Machine Systems.
[13] Scott E Crouter,et al. Estimating physical activity in youth using a wrist accelerometer. , 2015, Medicine and science in sports and exercise.
[14] Diane J. Cook,et al. A survey of methods for time series change point detection , 2017, Knowledge and Information Systems.
[15] Rasool Jalili,et al. FAST: Fast Anonymization of Big Data Streams , 2014, BigDataScience '14.
[16] Huaijiang Sun,et al. Automated human motion segmentation via motion regularities , 2013, The Visual Computer.
[17] Lina Yao,et al. Unobtrusive Posture Recognition via Online Learning of Multi-dimensional RFID Received Signal Strength , 2015, 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS).
[18] Didier Stricker,et al. Introducing a New Benchmarked Dataset for Activity Monitoring , 2012, 2012 16th International Symposium on Wearable Computers.
[19] Peter Grosche,et al. Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity , 2014, IEEE Transactions on Multimedia.
[20] A. Campbell,et al. Progress in Artificial Intelligence , 1995, Lecture Notes in Computer Science.
[21] John Staudenmayer,et al. A method to estimate free-living active and sedentary behavior from an accelerometer. , 2014, Medicine and science in sports and exercise.
[22] Eamonn J. Keogh,et al. Matrix Profile I: All Pairs Similarity Joins for Time Series: A Unifying View That Includes Motifs, Discords and Shapelets , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[23] David S. Matteson,et al. A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data , 2013, 1306.4933.
[24] Alípio Mário Jorge,et al. Progress in Artificial Intelligence , 2002, Lecture Notes in Computer Science.
[25] Andreas Reinhardt,et al. Predicting the Power Consumption of Electric Appliances through Time Series Pattern Matching , 2013, BuildSys@SenSys.
[26] Masaaki Itoh,et al. Development of a catalytic cracking process for converting waste plastics to petrochemicals , 2003 .
[27] J. Staudenmayer,et al. Validation of wearable monitors for assessing sedentary behavior. , 2011, Medicine and science in sports and exercise.
[28] Peter F. Stadler,et al. Similarity-Based Segmentation of Multi-Dimensional Signals , 2017, Scientific Reports.
[29] C. Wingo,et al. Hypokalemia--consequences, causes, and correction. , 1997, Journal of the American Society of Nephrology : JASN.
[30] T. Sejnowski,et al. Non-Linear Dynamical Analysis of EEG Time Series Distinguishes Patients with Parkinson’s Disease from Healthy Individuals , 2013, Front. Neurol..
[31] Christos Faloutsos,et al. AutoPlait: automatic mining of co-evolving time sequences , 2014, SIGMOD Conference.
[32] Roger G. Mark,et al. Circulatory response to passive and active changes in posture , 2003, Computers in Cardiology, 2003.
[33] Gunnar Rätsch,et al. Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs , 2017, ArXiv.
[34] Eamonn J. Keogh,et al. On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.
[35] Eamonn J. Keogh,et al. Segmenting Time Series: A Survey and Novel Approach , 2002 .
[36] Laurent Itti,et al. Decomposing time series with application to temporal segmentation , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).
[37] Trevor P Martin,et al. Intelligent Data Engineering and Automated Learning , 2004 .
[38] Vladimir Pavlovic,et al. Learning Switching Linear Models of Human Motion , 2000, NIPS.
[39] Eamonn J. Keogh,et al. Towards never-ending learning from time series streams , 2013, KDD.
[40] Dana Kulic,et al. Segmentation of human upper body movement using multiple IMU sensors , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[41] N G Pandian,et al. Diagnosis of cardiac tamponade after cardiac surgery: relative value of clinical, echocardiographic, and hemodynamic signs. , 1994, American heart journal.
[42] Jesús García,et al. Segmentation and Classification of Time-Series: Real Case Studies , 2009, IDEAL.