Working out when something you are seeing is not something you were expecting, when you are not quite sure how to quantify what you expect actually. Detecting black swans, for example.
Nuit Blanche rounds up methods for high dimensional statistics.
e.g. Gnip Trend detection Nikolov (2012) looks fun, and comes with a cute explanation of how you might do this nonparametrically.
Sevvandi Kandanaarachchi is presenting some methods in a seminar right now: (Kandanaarachchi and Hyndman 2021; Kandanaarachchi, Hyndman, and Smith-Miles 2020; Kandanaarachchi 2021; Sevvandi Kandanaarachchi and Hyndman 2021; Talagala et al. 2020).
Ahmad, Subutai, Alexander Lavin, Scott Purdy, and Zuha Agha. 2017. “Unsupervised Real-Time Anomaly Detection for Streaming Data.” Neurocomputing, Online Real-Time Learning Strategies for Data Streams, 262 (November): 134–47.
Chalapathy, Raghavendra, and Sanjay Chawla. 2019. “Deep Learning for Anomaly Detection: A Survey.” arXiv:1901.03407 [Cs, Stat], January.
Henrickson, Scott, Jeff Kolb, Brian Lehmann, and Joshua Montague. 2015. “Trend Detection in Social Data.” Twitter.
Kandanaarachchi, Sevvandi. 2021. “Unsupervised Anomaly Detection Ensembles Using Item Response Theory.” arXiv:2106.06243 [Cs, Stat], June.
Kandanaarachchi, Sevvandi, and Rob J. Hyndman. 2021. “Dimension Reduction for Outlier Detection Using DOBIN.” Journal of Computational and Graphical Statistics 30 (1): 204–19.
Kandanaarachchi, Sevvandi, Rob J. Hyndman, and Kate Smith-Miles. 2020. “Early Classification of Spatio-Temporal Events Using Partial Information.” PLOS ONE 15 (8): e0236331.
Kandanaarachchi, Sevvandi, Mario A. Munoz, Rob J. Hyndman, and Kate Smith-Miles. 2018. “On Normalization and Algorithm Selection for Unsupervised Outlier Detection.” 16/18. Monash Econometrics and Business Statistics Working Papers. Monash University, Department of Econometrics and Business Statistics.
Kristiadi, Agustinus, Matthias Hein, and Philipp Hennig. 2022. “Being a Bit Frequentist Improves Bayesian Neural Networks.” In CoRR. arXiv.
Kulinski, Sean, and David I. Inouye. 2022. “Towards Explaining Distribution Shifts.” arXiv.
Lopez, Jose A., Octavia Camps, and Mario Sznaier. 2015. “Robust Anomaly Detection Using Semidefinite Programming.” arXiv:1504.00905 [Cs, Math], April.
Ming, Yifei, Ying Fan, and Yixuan Li. 2022. “POEM: Out-of-Distribution Detection with Posterior Sampling,” June.
Nikolov, Stanislav. 2012. “Trend or no trend : a novel nonparametric method for classifying time series.” Thesis, Massachusetts Institute of Technology.
Ovadia, Yaniv, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, and Jasper Snoek. 2019. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.” In Proceedings of the 33rd International Conference on Neural Information Processing Systems, 14003–14. Red Hook, NY, USA: Curran Associates Inc.
Pang, Guansong, Chunhua Shen, Longbing Cao, and Anton van den Hengel. 2021. “Deep Learning for Anomaly Detection: A Review.” ACM Computing Surveys 54 (2): 1–38.
Sevvandi Kandanaarachchi, and Rob J Hyndman. 2021. “Leave-One-Out Kernel Density Estimates for Outlier Detection.”
She, Yiyuan, and Art B. Owen. 2010. “Outlier Detection Using Nonconvex Penalized Regression.”
Talagala, Priyanga Dilini, Rob J. Hyndman, Kate Smith-Miles, Sevvandi Kandanaarachchi, and Mario A. Muñoz. 2020. “Anomaly Detection in Streaming Nonstationary Temporal Data.” Journal of Computational and Graphical Statistics 29 (1): 13–27.
No comments yet. Why not leave one?