Anomaly Detection for an E-commerce Pricing System

PyOD: A Python Toolbox for Scalable Outlier Detection

Original

Yue Zhao, Zain Nasrullah, and Zheng Li. 2019 · 1901

Earlier work this paper cites.

Generic and Scalable Framework for Automated Time-series Anomaly Detection. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’15) . ACM, New York, NY, USA, 1939–1947

Nikolay Laptev, Saeed Amizadeh, and Ian Flint. 2015 · 1947

Earlier work this paper cites.

Support Vector Method for Novelty Detection. In Proceedings of the 12th International Conference on Neural Information Processing Systems (NIPS’99) . MIT Press, Cambridge, MA, USA, 582–588

Bernhard Schölkopf, Robert Williamson, Alex Smola, John Shawe-Taylor, and John Platt. 1999 · 1999

Earlier work this paper cites.

LOF: Identifying Density-based Local Outliers

Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, and Jörg Sander. 2000 · 2000

Earlier work this paper cites.

Random Forests

Leo Breiman. 2001 · 2001

Earlier work this paper cites.

Greedy function approximation: A gradient boosting machine

Jerome H. Friedman. 2001 · 2001

Earlier work this paper cites.

Estimating the Support of a High-Dimensional Distribution

Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, and Robert C. Williamson. 2001 · 2001

Earlier work this paper cites.

Fast Outlier Detection in High Dimensional Spaces. In Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD ’02) . Springer-Verlag, London, UK, UK, 15–26

Fabrizio Angiulli and Clara Pizzuti. 2002 · 2002

Earlier work this paper cites.

Support Vector Data Description

David M.J. Tax and Robert P.W. Duin. 2004 · 2004

Earlier work this paper cites.

Detecting anomalous patterns in pharmacy retail data

Maheshkumar R Sabhnani, Daniel B Neill, and Andrew W Moore. 2005 · 2005

Earlier work this paper cites.

NumPy: A guide to NumPy

Travis Oliphant. 2006– · 2006

Earlier work this paper cites.

Hierarchical Maximum Entropy Density Estimation. In Proceedings of the 24th International Conference on Machine Learning (ICML ’07) . ACM, New York, NY, USA, 249–256

Miroslav Dudik, David M. Blei, and Robert E. Schapire. 2007 · 2007

Earlier work this paper cites.

Angle-based Outlier Detection in High-dimensional Data. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’08) . ACM, New York, NY, USA, 444–452

Hans-Peter Kriegel, Matthias Schubert, and Arthur Zimek. 2008 · 2008

Cited alongside, same era.

Isolation Forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining (ICDM ’08) . IEEE Computer Society, Washington, DC, USA, 413–422

Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008 · 2008

Cited alongside, same era.

EGMM: An enhanced Gaussian mixture model for detecting moving objects with intermittent stops

Huiyuan Fu, Huadong Ma, and Anlong Ming. 2011 · 2011

Cited alongside, same era.

Robust Kernel Density Estimation

JooSeuk Kim and Clayton D. Scott. 2011 · 2011

Cited alongside, same era.

Scikit-learn: Machine Learning in Python

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011

Outlier Analysis (2nd ed.)

Charu C. Aggarwal. 2016 · 2016

Later among the works it cites.

Real-Time Anomaly Detection for Streaming Analytics

Original

Subutai Ahmad and Scott Purdy. 2016 · 2016

Later among the works it cites.

XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16) . ACM, New York, NY, USA, 785–794

Tianqi Chen and Carlos Guestrin. 2016 · 2016

Later among the works it cites.

Loda: Lightweight On-line Detector of Anomalies

Tomáš Pevn? 2016 · 2016

Later among the works it cites.

Deep Structured Energy Based Models for Anomaly Detection. In Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (ICML’16) . JMLR.org, 1100–1109

Shuangfei Zhai, Yu Cheng, Weining Lu, and Zhongfei Zhang. 2016 · 2016