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Authors
# Name
1 Rebecca Salles(rebeccapsalles@acm.org)
2 Luciana Escobar(luciana.vignoli@eic.cefet-rj.br)
3 Lais Baroni(lais.baroni}@eic.cefet-rj.br)
4 Rocio Zorrilla(romizc@lncc.br)
5 Artur Ziviani(ziviani@lncc.br)
6 Vincius Kreischer(vinicius.kreischer@lncc.br)
7 Flavia Delicato(fdelicato@ic.uff.br)
8 Paulo Pires(paulo.pires@ic.uff.br)
9 Luciano Maia( luciano.maia@petrobras.com.br)
10 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)
11 Laura Assis(laura.assis@cefet-rj.br)
12 Eduardo Ogasawara( eogasawara@ieee.org)

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Reference
# Reference
1 E. Calikus, S. Nowaczyk, A. Sant’Anna, and O. Dikmen. No free lunch but a cheaper supper: A general framework for streaming anomaly detection. Expert Systems with Applications, 155:113453, October 2020. ISSN 0957-4174.
2 R. Carmona. Statistical Analysis of Financial Data in R. Springer-Verlag New York, 2014.
3 H. Chen and N. Zhang. Graph-based change-point detection. The Annals of Statistics, 43(1):139–176, February 2015. ISSN 0090-5364.
4 D. De Paepe, S. V. Hautte, B. Steenwinckel, F. De Turck, F. Ongenae, O. Janssens, and S. V. Hoecke. A generalized matrix profile framework with support for contextual series analysis. Engineering Applications of Artificial Intelligence, 90, 2020. ISSN 0952-1976.
5 B. Eriksson, P. Barford, R. Bowden, N. Duffield, J. Sommers, and M. Roughan. Basisdetect: A model-based network event detection framework. In Proceedings of the 10th ACM SIGCOMM, page 451–464, New York, NY, USA, 2010. Association for Computing Machinery.
6 M. Gupta, J. Gao, C. Aggarwal, and J. Han. Outlier Detection for Temporal Data: A Survey. IEEE Transactions on Knowledge and Data Engineering, 2014. ISSN 1041-4347.
7 V. Guralnik and J. Srivastava. Event Detection from Time Series Data. In Proceedings of the Fifth ACM SIGKDD, KDD ’99, pages 33–42, New York, NY, USA, 1999. ACM. ISBN 978-1-58113-143-7.
8 G. Lu, Y. Zhou, C. Lu, and X. Li. A novel framework of change-point detection for machine monitoring. Mechanical Systems and Signal Processing, 83, 07 2016.
9 H. Raza, G. Prasad, and Y. Li. EWMA Model Based Shift-Detection Methods for Detecting Covariate Shifts in Non-Stationary Environments. Pattern Recogn., 48(3):659–669, March 2015. ISSN 0031-3203.
10 F. Rehbach, S. Moritz, S. Chandrasekaran, M. Rebolledo, M. Friese, and T. Bartz-Beielstein. GECCO 2018 Industrial Challenge: Monitoring of drinking-water quality. pages 1–7, 2018.
11 R. Salles, K. Belloze, F. Porto, P.H. Gonzalez, and E. Ogasawara. Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems, 164:274–291, 2019.
12 D. Silva, A. Simões, C. Cardoso, D. E. M. Oliveira, Y. Souto, L. E. G. Vignoli, R. Salles, H. S. C. Jr, A. Ziviani, E. Ogasawara, F. C. Delicato, P. F. Pires, H. L. C. P. Pinto, L. Maia, and F. Porto. A conceptual vision toward the management of machine learning models. In Proceedings of the ER Forum 2019, Salvador, Bahia, Brazil, volume 2469, pages 15–27, 2019.
13 J.-I. Takeuchi and K. Yamanishi. A unifying framework for detecting outliers and change points from time series. IEEE Transactions on Knowledge and Data Engineering, 18(4):482–492, 2006.
14 P. D. Talagala, R. J. Hyndman, K. Smith-Miles, S. Kandanaarachchi, and M. Muñoz. Anomaly Detection in Streaming Nonstationary Temporal Data. Journal of Computational and Graphical Statistics, 29(1):13–27, 2020.
15 Yahoo! Webscope. Labeled anomaly detection dataset. March 2015.
16 L. Xiong, C. Jiang, C. Xu, K. Yu, and S. Guo. A framework of change-point detection for multivariate hydrological series. Water Resources Research, 51, 09 2015.