Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais
Resumo
Ao analisar séries temporais é possível observar mudanças significativas no comportamento das observações que frequentemente caracterizam a ocorrência de eventos. Eventos se apresentam como anomalias, pontos de mudança, ou padrões frequentes. Na literatura existem diversos métodos para detecção de eventos. Entretanto, a busca por um método adequado para uma série temporal não é uma tarefa simples, principalmente considerando-se que a natureza dos eventos muitas vezes não é conhecida. Neste contexto, este trabalho apresenta Harbinger, um framework para integração e análise de métodos de detecção de eventos. O Harbinger foi avaliado em dados sintéticos e reais, onde foi possível constatar que suas funcionalidades promovem a seleção de métodos e a compreensão dos eventos detectados.
Palavras-chave:
séries temporais, detecção de eventos, framework, anomalias, outliers, pontos de mudança, métodos de detecção, modelos de detecção
Referências
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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.
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.
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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.
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.
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.
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.
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.
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.
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.
Yahoo! Webscope. Labeled anomaly detection dataset. March 2015.
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.
R. Carmona. Statistical Analysis of Financial Data in R. Springer-Verlag New York, 2014.
H. Chen and N. Zhang. Graph-based change-point detection. The Annals of Statistics, 43(1):139–176, February 2015. ISSN 0090-5364.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Yahoo! Webscope. Labeled anomaly detection dataset. March 2015.
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.
Publicado
28/09/2020
Como Citar
SALLES, Rebecca et al.
Harbinger: Um framework para integração e análise de métodos de detecção de eventos em séries temporais. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 35. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 73-84.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2020.13626.
