Fetching the paper…
Reading the bibliography…
Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains.
D. J. Bemdt and J. Clifford, “Using Dynamic Time Warping to Find Patterns in Time Series,” in AAAI Workshop on Knowledge Discovery in Databases , Seattle, Washington, 1994
1994
Earlier work this paper cites.
D. Decoste, “Automated Learning and Monitoring of Limit Functions,” in International Symposium on Artificial Intelligence, Robotics, and Automation in Space , 1997
1997
Earlier work this paper cites.
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “Lof: identifying density-based local outliers,” in Proc. of the ACM SIGMOD , 2000, pp. 93–104
2000
Earlier work this paper cites.
F. Angiulli and C. Pizzuti, “Fast outlier detection in high dimensional spaces,” in European Conference on Principles of Data Mining and Knowledge Discovery . Springer, 2002, pp. 15–27
2002
Earlier work this paper cites.
Z. He, X. Xu, and S. Deng, “Discovering cluster-based local outliers,” Pattern Recognition Letters , vol. 24, no. 9-10, pp. 1641–1650, 2003
2003
Earlier work this paper cites.
P. De Chazal, M. O’Dwyer, and R. B. Reilly, “Automatic classification of heartbeats using ecg morphology and heartbeat interval features,” IEEE transactions on biomedical engineering , vol. 51, no. 7, pp. 1196–1206, 2004
2004
Earlier work this paper cites.
V. Hodge and J. Austin, “A survey of outlier detection methodologies,” Artificial intelligence review , vol. 22, no. 2, pp. 85–126, 2004
2004
Earlier work this paper cites.
H. Ringberg, A. Soule, J. Rexford, and C. Diot, “Sensitivity of pca for traffic anomaly detection,” in Proc. of the 2007 ACM SIGMETRICS , 2007, pp. 109–120
2007
Earlier work this paper cites.
V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM computing surveys (CSUR) , vol. 41, no. 3, p. 15, 2009
2009
Earlier work this paper cites.
J. M. Torres, P. G. Nieto, L. Alejano, and A. Reyes, “Detection of outliers in gas emissions from urban areas using functional data analysis,” Journal of hazardous materials , vol. 186, no. 1, pp. 144–149, 2011
2011
Earlier work this paper cites.
E. H. Pena, M. V. de Assis, and M. L. Proença, “Anomaly detection using forecasting methods arima and hwds,” in International Conference of the Chilean Computer Science Society (SCCC) , 2013, pp. 63–66
2013
Earlier work this paper cites.
X. Dai and Z. Gao, “From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis,” IEEE Transactions on Industrial Informatics , vol. 9, no. 4, pp. 2226–2238, 2013
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative Adversarial Nets,” in Proc. of Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
J.-A. Martínez-Heras and A. Donati, “Enhanced Telemetry Monitoring with Novelty Detection,” AI Magazine , vol. 35, no. 4, p. 37, 2014
2014
Earlier work this paper cites.
G. E. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control . John Wiley & Sons, 2015
2015
Earlier work this paper cites.
P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long Short Term Memory Networks for Anomaly Detection in Time Series,” in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , 2015
2015
Cited alongside, same era.
J. An and S. Cho, “Variational autoencoder based anomaly detection using reconstruction probability,” Special Lecture on IE , vol. 2, no. 1, 2015
2015
Cited alongside, same era.
A. Lavin and S. Ahmad, “Evaluating real-time anomaly detection algorithms–the numenta anomaly benchmark,” in Proc. of IEEE ICMLA , 2015, pp. 38–44
2015
Cited alongside, same era.
D. Zheng, F. Li, and T. Zhao, “Self-adaptive statistical process control for anomaly detection in time series,” Expert Systems with Applications , vol. 57, pp. 324–336, 2016
2016
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proc. of the 34th ICML , 2017, pp. 214–223
2017
Later among the works it cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. Courville, “Improved Training of Wasserstein GANs,” in Proc. of the 31st Int. Conf. on Neural Information Processing Systems , 2017, pp. 5769–5779
2017
Later among the works it cites.
K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding,” in Proc. of the 24th ACM SIGKDD , 2018
2018
Later among the works it cites.
L. Deecke, R. Vandermeulen, L. Ruff, S. Mandt, and M. Kloft, “Anomaly detection with generative adversarial networks,” 2018. [Online]. Available: https://openreview.net/forum?id=S1EfylZ0Z
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, “LSTM-based encoder-decoder for multi-sensor anomaly detection,” in Anomaly Detection Workshop at 33rd ICML , 2016
2016
Cited alongside, same era.
M. Goldstein and S. Uchida, “A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data,” PLOS ONE , vol. 11, no. 4, pp. 1–31, 04 2016
2016
Cited alongside, same era.
C. Vondrick, H. Pirsiavash, and A. Torralba, “Generating videos with scene dynamics,” in Proc. of Advances in neural information processing systems , 2016, pp. 613–621
2016
Cited alongside, same era.
A. Makhzani, J. Shlens, N. Jaitly, and I. Goodfellow, “Adversarial autoencoders,” in Proc. of ICLR, Workshop Track , 2016
2016
Cited alongside, same era.
S. Ahmad, A. Lavin, S. Purdy, and Z. Agha, “Unsupervised real-time anomaly detection for streaming data,” Neurocomputing , vol. 262, pp. 134–147, 2017
2017
Cited alongside, same era.
D. Kwon, H. Kim, J. Kim, S. C. Suh, I. Kim, and K. J. Kim, “A survey of deep learning-based network anomaly detection,” Cluster Computing , vol. 22, pp. 949–961, 2017
2017
Cited alongside, same era.
J. Goh, S. Adepu, M. Tan, and Z. S. Lee, “Anomaly detection in cyber physical systems using recurrent neural networks,” in IEEE 18th International Symposium on High Assurance Systems Engineering (HASE) , 2017, pp. 140–145
2017
Cited alongside, same era.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks,” in IEEE Int. Conf. on Computer Vision (ICCV) , oct 2017, pp. 2242–2251
2017
Cited alongside, same era.
H. Zenati, M. Romain, C.-S. Foo, B. Lecouat, and V. Chandrasekhar, “Adversarially Learned Anomaly Detection,” in IEEE ICDM , nov 2018, pp. 727–736
2018
Later among the works it cites.
D. Li, D. Chen, B. Jin, L. Shi, J. Goh, and S.-K. Ng, “Mad-gan: Multivariate anomaly detection for time series data with generative adversarial networks,” in International Conference on Artificial Neural Networks . Springer, 2019, pp. 703–716
2019
Later among the works it cites.
H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang, “Time-series anomaly detection service at microsoft,” in Proc. of the 25th ACM SIGKDD , 2019, pp. 3009–3017
2019
Later among the works it cites.
D. Salinas, V. Flunkert, J. Gasthaus, and T. Januschowski, “Deepar: Probabilistic forecasting with autoregressive recurrent networks,” International Journal of Forecasting , 2019
2019
Later among the works it cites.
J. Yoon, D. Jarrett, and M. van der Schaar, “Time-series generative adversarial networks,” in Proc. of Advances in Neural Information Processing Systems . Curran Associates, Inc., 2019, pp. 5509–5519
2019
Later among the works it cites.
H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P.-A. Muller, “Deep learning for time series classification: a review,” Data Mining and Knowledge Discovery , vol. 33, no. 4, pp. 917–963, 2019
2019
Later among the works it cites.
J. Qiu, Q. Du, and C. Qian, “Kpi-tsad: A time-series anomaly detector for kpi monitoring in cloud applications,” Symmetry , vol. 11, no. 11, p. 1350, 2019
2019
Later among the works it cites.
R. A. A. Habeeb, F. Nasaruddin, A. Gani, I. A. T. Hashem, E. Ahmed, and M. Imran, “Real-time big data processing for anomaly detection: A survey,” International Journal of Information Management , vol. 45, pp. 289–307, 2019
2019
Later among the works it cites.
B. Zhou, S. Liu, B. Hooi, X. Cheng, and J. Ye, “BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series,” in Proc. of the 28th Int. Joint Conf. on Artificial Intelligence, (IJCAI) , 2019, pp. 4433–4439
2019
Later among the works it cites.
T. Schlegl, P. Seeböck, S. M. Waldstein, G. Langs, and U. Schmidt-Erfurth, “f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks,” Medical Image Analysis , vol. 54, pp. 30 – 44, 2019
2019
Later among the works it cites.