Applications of Signature Methods to Market Anomaly Detection
Department of Mathematics, ETH, Zurich, Switzerland（苏黎世联邦理工学院数学系，瑞士苏黎世）；Department of Banking and Finance, University of Zurich, Zurich, Switzerland（苏黎世大学银行与金融系，瑞士苏黎世）
Erdinc Akyildirim, Matteo Gambara, Josef Teichmann, Syang Zhou
Anomaly detection is the process of identifying abnormal instances or events in data sets which deviate from the norm significantly. In this study, we propose a signatures based machine learning algorithm to detect rare or unexpected items in a given data set of time series type. We present applications of signature or randomized signature as feature extractors for anomaly detection algorithms; additionally we provide an easy, representation theoretic justification for the construction of randomized signatures. Our first application is based on synthetic data and aims at distinguishing between real and fake trajectories of stock prices, which are indistinguishable by visual inspection. We also show a real life application by using transaction data from the cryptocurrency market. In this case, we are able to identify pump and dump attempts organized on social networks with F1 scores up to 88% by means of our unsupervised learning algorithm, thus achieving results that are close to the state-of-the-art in the field based on supervised learning.
Norwegian University of Science and Technology（挪威科技大学）；Sintef Energy Research（Sintef能源研究所）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；University of Stavanger（斯塔万格大学）
Dive into Time-Series Anomaly Detection: A Decade Review
Inria（法国国家信息与自动化研究所）；DI ENS（巴黎高等师范学院数字研究所）；PSL（巴黎文理研究大学）；CNRS（法国国家科学研究中心）；The Ohio State University（俄亥俄州立大学）；Université Paris Cité（巴黎西岱大学）；IUF（法国大学研究院）
Université Clermont Auvergne, CNRS, LPCA（克莱蒙特奥弗涅大学、法国国家科学研究中心、LPCA研究所）；National Research University Higher School of Economics（俄罗斯国民研究大学高等经济学院）；The Oskar Klein Centre, Department of Astronomy, Stockholm University（斯德哥尔摩大学奥斯特基尔克中心、天文学系）；Faculty of Physics, Lomonosov Moscow State University（莫斯科罗蒙诺索夫莫斯科国立大学物理系）；McWilliams Center for Cosmology and Astrophysics, Department of Physics, Carnegie Mellon University（卡内基梅隆大学天体物理学与宇宙学中心、物理系）；Sternberg Astronomical Institute, Lomonosov Moscow State University（莫斯科罗蒙诺索夫莫斯科国立大学斯特尔贝格天文研究所）；Physics department, University of Surrey（萨里大学物理系）；Space Research Institute of the Russian Academy of Sciences（俄罗斯科学院空间研究 institutes）
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
ETH Zurich（苏黎世联邦理工学院）；California Institute of Technology（加州理工学院）；CERN（欧洲核子研究组织）
我们应用逆向学习异常检测（ALAD）算法在大型强子对撞机的质子-质子碰撞中发现新的物理过程的问题。基于ALAD的异常检测与变分自编码器达到的性能相匹配，在某些情况下有很大的改进。在4.4 fb-1的8 TeV CMS开放数据上训练ALAD算法，我们展示了数据驱动的异常检测和表征如何在现实生活中工作，通过识别LHC的t-tbar实验签名的主要特征来重新发现顶夸克。
