{"type":"ScholarlyArticle","title":"Stochastic Online Anomaly Analysis for Streaming Time Series","authors":[{"name":"Zhao Xu","slug":"zhao-xu","given":"Zhao","family":"Xu","affiliation":"NEC Labs Europe , Germany"},{"name":"Kristian Kersting","slug":"kristian-kersting","given":"Kristian","family":"Kersting","affiliation":"Technical University of Darmstadt , Germany"},{"name":"Lorenzo von Ritter","slug":"lorenzo-von-ritter","given":"Lorenzo","family":"von Ritter","affiliation":"NEC Labs Europe , Germany; Technical University of Munich , Germany"}],"abstract":"Identifying patterns in time series that exhibit anomalous behavior is of increasing importance in many domains, such as financial and Web data analysis. In real applications, time series data often arrive continuously, and usually only a single scan is allowed through the data. Batch learning and retrospective segmentation methods would not be well applicable to such scenarios. In this paper, we present an online nonparametric Bayesian method OLAD for anomaly analysis in streaming time series. Moreover, we develop a novel and efficient online learning approach for the OLAD model based on stochastic gradient descent. The proposed method can effectively learn the underlying dynamics of anomaly-contaminated heavy-tailed time series and identify potential anomalous events. Empirical analysis on real-world datasets demonstrates the effectiveness of our method.","doi":"10.24963/ijcai.2017/445","url":"https://ijcai.org/proceedings/2017/445","pdf":"https://ijcai.org/proceedings/2017/0445.pdf","markdown":"https://ijcai.org/proceedings/2017/445.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/445","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":445,"pages":{"first":3189,"last":3195},"keywords":["Machine Learning: Machine Learning","Machine Learning: Time-series/Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal, 2013] Charu Aggarwal. Outlier analysis. Springer, 2013.","title":"Outlier analysis","authors":"Charu Aggarwal","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chandola et al., 2009] Varun Chandola, Arindam Banerjee, and Vipin Kumar. Anomaly detection: A survey. ACM Computing Surveys, 41(3):15:1-15:58, 2009.","title":"Anomaly detection: A survey","authors":"Varun Chandola, Arindam Banerjee, Vipin Kumar","year":"2009","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Duchi et al., 2011] John Duchi, Elad Hazan, and Yoram Singer. Adaptive subgradient methods for online learn- ing and stochastic optimization. The Journal of Machine Learning Research, 12:2121-2159, 2011.","title":"Adaptive subgradient methods for online learn-ing and stochastic optimization","authors":"John Duchi, Elad Hazan, Yoram Singer","year":"2011","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Görnitz et al., 2015] Nico Görnitz, Mikio Braun, and Mar- ius Kloft. Hidden markov anomaly detection. In Proceed- ings of the 32 nd International Conference on Machine Learning, 2015.","title":"Hidden markov anomaly detection","authors":"Nico Görnitz, Mikio Braun, Marius Kloft","year":"2015","venue":"Proceedings of the 32 nd International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gupta et al., 2014] Manish Gupta, Jing Gao, Charu Aggar- wal, and Jiawei Han. Outlier detection for temporal data: a survey. IEEE Transaction on Knowledge and Data En- gineering, 25(1), 2014.","title":"Outlier detection for temporal data: a survey","authors":"Manish Gupta, Jing Gao, Charu Aggarwal, Jiawei Han","year":"2014","venue":"IEEE Transaction on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hoffman et al., 2013] Matthew Hoffman, David Blei, Chong Wang, and John William Paisley. Stochastic variational inference. Journal of Machine Learning Research, 14(1):13031347, 2013.","title":"Stochastic variational inference","authors":"Matthew Hoffman, David Blei, Chong Wang, John William Paisley","year":"2013","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Lakhina et al., 2004] Anukool Lakhina, Mark Crovella, and Christophe Diot. Diagnosing network-wide traffic anoma- lies. In Proceedings of SIGCOMM, 2004.","title":"Diagnosing network-wide traffic anomalies","authors":"Lakhina","year":"2004","venue":"Proceedings of SIGCOMM","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ma and Perkins, 2003] Junshui Ma and Simon Perkins. Time-series novelty detection using one-class support vec- tor machines. In Proceedings of the International Joint Conference on Neural Networks, 2003.","title":"Junshui Ma and Simon Perkins. Time-series novelty detection using one-class support vector machines","authors":"Perkins Ma","year":"2003","venue":"Proceedings of the International Joint Conference on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Nadarajah and Kotz, 2008] Saralees Nadarajah and Samuel Kotz. Estimation methods for the multivariate t distri- bution. Acta Applicandae Mathematicae, 102(1):99-118, 2008.","title":"Saralees Nadarajah and Samuel Kotz. Estimation methods for the multivariate t distribution","authors":"Kotz Nadarajah","year":"2008","venue":"Acta Applicandae Mathematicae","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Osborne et al., 2012] Michael Osborne, Stephen Roberts, Alex Rogers, and Nicholas Jennings. Real-time informa- tion processing of environmental sensor network data us- ing bayesian gaussian processes. ACM Transactions on Sensor Networks (TOSN), 9(1):1-32, 2012.","title":"Real-time information processing of environmental sensor network data using bayesian gaussian processes","authors":"Michael Osborne, Stephen Roberts, Alex Rogers, Nicholas Jennings","year":"2012","venue":"ACM Transactions on Sensor Networks (TOSN)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Pimentel et al., 2014] Marco Pimentel, David Clifton, Lei Clifton, and Lionel Tarassenko. A review of novelty de- tection. Signal Processing, 99:215-249, 2014.","title":"A review of novelty detection","authors":"Marco Pimentel, David Clifton, Lei Clifton, Lionel Tarassenko","year":"2014","venue":"Signal Processing","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Rasmussen and Williams, 2006] Carl Rasmussen and Christopher Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006.","title":"Gaussian Processes for Machine Learning","authors":"Williams ; Rasmussen, Carl Rasmussen, Christopher Williams","year":"2006","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Robbins and Monro, 1951] Herbert Robbins and Sutton Monro. A stochastic approximation method. The annals of mathematical statistics, pages 400-407, 1951.","title":"The annals of mathematical statistics","authors":"Monro Robbins, Herbert Robbins, Sutton Monro","year":"1951","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Roberts et al., 2013] Stephen Roberts, Michael Osborne, Mark Ebden, Steven Reece, Neale Gibson, and Suzanne Aigrain. Gaussian processes for time series modelling. Philosophical Transactions of the Royal Society of London A, 371(1984), 2013.","title":"Gaussian processes for time series modelling","authors":"Stephen Roberts, Michael Osborne, Mark Ebden, Steven Reece, Neale Gibson, Suzanne Aigrain","year":"1984","venue":"Philosophical Transactions of the Royal Society of London A","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Roth, 2013] Michael Roth. On the multivariate t distri- bution. Technical Report LiTH-ISY-R-3059, Linköpings Universitet, 2013.","title":"On the multivariate t distribution","authors":"Michael Roth","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Saada and Meng, 2012] Mohamad Saada and Qinggang Meng. An efficient algorithm for anomaly detection in a flight system using dynamic bayesian networks. In Neural Information Processing, Volume 7665 of the series Lecture Notes in Computer Science, 2012.","title":"An efficient algorithm for anomaly detection in a flight system using dynamic bayesian networks","authors":"Meng Saada, Mohamad Saada, Qinggang Meng","year":"2012","venue":"Neural Information Processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Saligrama and Zhao, 2012] Venkatesh Saligrama and Manqi Zhao. Local anomaly detection. In Proceed- ings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.","title":"Local anomaly detection","authors":"Venkatesh Saligrama, Manqi Zhao","year":"2012","venue":"Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Seeger, 2004] Matthias Seeger. Low rank updates for the cholesky decomposition. Technical report, University of California, Berkeley, 2004.","title":"Low rank updates for the cholesky decomposition","authors":"Matthias Seeger","year":"2004","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Shah et al., 2014] Amar Shah, Andrew Wilson, and Zoubin Ghahramani. Student-t processes as alternatives to gaus- sian processes. In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AIS- TATS), 2014.","title":"Student-t processes as alternatives to gaussian processes","authors":"Amar Shah, Andrew Wilson, Zoubin Ghahramani","year":"2014","venue":"Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AIS-TATS)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Smith et al., 2012] Mark Smith, Steven Reece, Stephen Roberts, and Iead Rezek. Online maritime abnormality de- tection using gaussian processes and extreme value theory. In Proceedings of the 12th IEEE International Conference on Data Mining (ICDM), pages 645-654, 2012.","title":"Online maritime abnormality detection using gaussian processes and extreme value theory","authors":"Mark Smith, Steven Reece, Stephen Roberts, Iead Rezek","year":"2012","venue":"Proceedings of the 12th IEEE International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Vallis et al., 2014] Owen Vallis, Jordan Hochenbaum, and Arun Kejariwal. A novel technique for long-term anomaly detection in the cloud. In Proceedings of the 6th USENIX Workshop on Hot Topics in Cloud Computing, 2014.","title":"A novel technique for long-term anomaly detection in the cloud","authors":"Owen Vallis, Jordan Hochenbaum, Arun Kejariwal","year":"2014","venue":"Proceedings of the 6th USENIX Workshop on Hot Topics in Cloud Computing","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Xu et al., 2016] Zhao Xu, Lorenzo von Ritter, and Kristian Kersting. Adaptive streaming anomaly analysis. In Pro- ceedings of NIPS 2016 Workshop on Artificial Intelligence for Data Science, 2016.","title":"Adaptive streaming anomaly analysis","authors":"Zhao Xu, Kristian Lorenzo Von Ritter, Kersting","year":"2016","venue":"Proceedings of NIPS 2016 Workshop on Artificial Intelligence for Data Science","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}