The Living Thing / Notebooks :

Anomaly detection

Working out when something you are seeing is not something you should have been expecting, in some sense which will not be made rigorous here, not yet.

Nuit Blanche has a roundup.

Special case: “trend detection”. e.g. Gnip Trend detection

BrSM09
Brauckhoff, D., Salamatian, K., & May, M. (2009) Applying PCA for Traffic Anomaly Detection: Problems and Solutions. In IEEE INFOCOM 2009 (pp. 2866–2870). DOI.
GCCP15
Girdhar, Y., Cho, W., Campbell, M., Pineda, J., Clarke, E., & Singh, H. (2015) Anomaly Detection in Unstructured Environments using Bayesian Nonparametric Scene Modeling. arXiv:1509.07979 [cs].
HKLM15
Henrickson, S., Kolb, J., Lehmann, B., & Montague, J. (2015) Trend detection in social data. . Twitter
ImWi97
Impagliazzo, R., & Wigderson, A. (1997) P = BPP if E Requires Exponential Circuits: Derandomizing the XOR Lemma. In Proceedings of the Twenty-ninth Annual ACM Symposium on Theory of Computing (pp. 220–229). New York, NY, USA: ACM DOI.
KGPR04
Kontostathis, A., Galitsky, L. M., Pottenger, W. M., Roy, S., & Phelps, D. J.(2004) A Survey of Emerging Trend Detection in Textual Data Mining. In M. W. Berry (Ed.), Survey of Text Mining (pp. 185–224). Springer New York
KoHP00
Kontostathis, A., Holzman, L. E., & Pottenger, W. M.(n.d.) Use of Term Clusters for Emerging Trend Detection.
LiYS15
Lindenbaum, O., Yeredor, A., & Salhov, M. (2015) Learning Coupled Embedding Using MultiView Diffusion Maps. In E. Vincent, A. Yeredor, Z. Koldovský, & P. Tichavský (Eds.), Latent Variable Analysis and Signal Separation (pp. 127–134). Springer International Publishing
LoCS15
Lopez, J. A., Camps, O., & Sznaier, M. (2015) Robust Anomaly Detection Using Semidefinite Programming. arXiv:1504.00905 [cs, Math].
Niko12

Nikolov, S. (2012) Trend or no trend : a novel nonparametric method for classifying time series (Thesis). . Massachusetts Institute of Technology

This one looks fun, and comes with a cute explanation of how you might do this nonparametrically.
PGKY15
Peng, Z., Gurram, P., Kwon, H., & Yin, W. (2015) Optimal Sparse Kernel Learning for Hyperspectral Anomaly Detection. arXiv:1506.02585 [cs].
RLPB06
Rajasegarar, S., Leckie, C., Palaniswami, M., & Bezdek, J. C.(2006) Distributed Anomaly Detection in Wireless Sensor Networks. In 10th IEEE Singapore International Conference on Communication systems, 2006. ICCS 2006 (pp. 1–5). DOI.
RSRD07
Ringberg, H., Soule, A., Rexford, J., & Diot, C. (2007) Sensitivity of PCA for Traffic Anomaly Detection. In Proceedings of the 2007 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems (pp. 109–120). New York, NY, USA: ACM DOI.
SoST05
Soule, A., Salamatian, K., & Taft, N. (2005) Combining Filtering and Statistical Methods for Anomaly Detection. In Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement (pp. 31–31). Berkeley, CA, USA: USENIX Association
WWZH15
Wang, D., Wu, P., Zhao, P., & Hoi, S. C. H.(2015) A Framework of Sparse Online Learning and Its Applications. arXiv:1507.07146 [cs].
WMCW02
Wong, W.-K., Moore, A., Cooper, G., & Wagner, M. (2002) Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks. In Eighteenth National Conference on Artificial Intelligence (pp. 217–223). Menlo Park, CA, USA: American Association for Artificial Intelligence
YWCM15
Yi, Wang, Chen, G., & Maggioni, M. (2015) High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies. arXiv:1509.07497 [stat].