An application of hierarchical Gaussian processes to the detection of anomalies in star light curves

Neurocomputing - Tập 342 - Trang 152-163 - 2019
Niall Twomey1, Haoyan Chen1, Tom Diethe1,2, Peter Flach1
1Intelligent Systems Laboratory, University of Bristol, UK
2Amazon Research, Cambridge, UK

Tài liệu tham khảo

Chandola, 2009, Anomaly detection: a survey, ACM Comput. Surv. (CSUR), 41, 15, 10.1145/1541880.1541882 Rebbapragada, 2008, Finding anomalous periodic time series, Mach. Learn., 74, 281, 10.1007/s10994-008-5093-3 Stetson, 1996, On the automatic determination of light-curve parameters for Cepheid variables, Publ. Astronom. Soc. Pac., 108, 851, 10.1086/133808 Udalski, 1997, Optical gravitational lensing experiment, OGLE-2--the second phase of the OGLE project Yankov, 2008, Disk aware discord discovery: finding unusual time series in terabyte sized datasets, Knowl. Inf. Syst., 17, 241, 10.1007/s10115-008-0131-9 Sterken, 2005 Rasmussen, 2005 Roberts, 2012, Gaussian processes for time-series modelling, Philos. Trans. R. Soc. Lond. A Math. Phys. Eng. Sci., 371 Hensman, 2013, Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters, BMC Bioinformat., 14, 252, 10.1186/1471-2105-14-252 Rakthanmanon, 2012, Searching and mining trillions of time series subsequences under dynamic time warping, 262 Malhotra, 2015, 89 Senin, 2014, Grammarviz 2.0: a tool for grammar-based pattern discovery in time series, 468 Ratsch, 2002, Constructing boosting algorithms from SVMs: an application to one-class classification, IEEE Trans. Pattern Anal. Mach. Intell., 24, 1184, 10.1109/TPAMI.2002.1033211 Aghabozorgi, 2015, Time-series clustering–a decade review, Inf. Syst., 53, 16, 10.1016/j.is.2015.04.007 Gupta, 2014, Outlier detection for temporal data: a survey, IEEE Trans. Knowl. Data Eng., 26, 2250, 10.1109/TKDE.2013.184 Domingos, 1999, The role of Occam’s razor in knowledge discovery, Data Mining Knowl. Discov., 3, 409, 10.1023/A:1009868929893 Myung, 1997, Applying Occam’s razor in modeling cognition: a Bayesian approach, Psychon. Bull. Rev., 4, 79, 10.3758/BF03210778 Rasmussen, 2001, Occam’s razor, 294 Bishop, 2006 Murphy, 2013 Roberts, 1999, Novelty detection using extreme value statistics, IEE Proc. Vis. Image Signal Process., 146, 124, 10.1049/ip-vis:19990428 Twomey, 2014, Automated detection of perturbed cardiac physiology during oral food allergen challenge in children, IEEE J. Biomed. Health Inform., 18, 1051, 10.1109/JBHI.2013.2290706 Mahadevan, 2010, Anomaly detection in crowded scenes, 1975 Akouemo, 2016, Probabilistic anomaly detection in natural gas time series data, Int. J. Forecast., 32, 948, 10.1016/j.ijforecast.2015.06.001 Xiang, 2008, Video behavior profiling for anomaly detection, IEEE Trans. Pattern Anal. Mach. Intell., 30, 893, 10.1109/TPAMI.2007.70731 Winn, 2015 Herbrich, 2007, Trueskill: a Bayesian skill rating system, 569 Flach, 2010, Novel tools to streamline the conference review process: experiences from SIGKDD’09, ACM SIGKDD Explor. Newsl., 11, 63, 10.1145/1809400.1809413 Stern, 2009, Matchbox: large scale online Bayesian recommendations, 111 D. Tran, A. Kucukelbir, A.B. Dieng, M. Rudolph, D. Liang, D.M. Blei, Edward: a library for probabilistic modeling, inference, and criticism, arXiv:1610.09787 (2016). Patil, 2010, PyMC: Bayesian stochastic modelling in python, J. Stat. Softw., 35, 1, 10.18637/jss.v035.i04 Carpenter, 2017, Stan: a probabilistic programming language, J. Stat. Softw., 76, 10.18637/jss.v076.i01 Dai, 2018, MXFusion: a modular deep probabilistic programming library Mascaro, 2014, Anomaly detection in vessel tracks using Bayesian networks, Int. J. Approx. Reason., 55, 84, 10.1016/j.ijar.2013.03.012 Ye, 2000, A Markov chain model of temporal behavior for anomaly detection, 166, 169 Darkins, 2013, Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm, PloS One, 8, e59795, 10.1371/journal.pone.0059795 E. Yu, P. Parekh, A Bayesian ensemble for unsupervised anomaly detection, arXiv:1610.07677 (2016). Neal, 2000, Markov chain sampling methods for Dirichlet process mixture models, J. Comput. Graph. Stat., 9, 249 Van Gael, 2009, The infinite HMM for unsupervised pos tagging, 678 GPy: A Gaussian process framework in Python, since 2012, (http://github.com/SheffieldML/GPy). Matthews, 2017, GPFlow: a Gaussian process library using tensorflow, J. Mach. Learn. Res., 18, 1299 S. Reece, R. Garnett, M. Osborne, S. Roberts, Anomaly detection and removal using non-stationary Gaussian processes, arXiv:1507.00566 (2015). W. Herlands, E. McFowland III, A.G. Wilson, D.B. Neill, Gaussian process subset scanning for anomalous pattern detection in non-iid data, arXiv:1804.01466 (2018). Chen, 2018, Anomaly detection in star light curves using hierarchical Gaussian processes, 615 Duvenaud, 2014 Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen, G. Batista, The UCR time series classification archive, 2015, www.cs.ucr.edu/~eamonn/time_series_data/. Mallat, 1999 Pelleg, 2000, X-means: extending k-means with efficient estimation of the number of clusters., 1, 727 Weakliem, 1999, A critique of the Bayesian information criterion for model selection, Sociol. Methods Res., 27, 359, 10.1177/0049124199027003002 Quiñonero-Candela, 2005, A unifying view of sparse approximate Gaussian process regression, J. Mach. Learn. Res., 6, 1939 Snelson, 2006, Sparse Gaussian processes using pseudo-inputs, 1257