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Authors
# Name
1 Edson Sobrinho(edsonpaulosobrinho@gmail.com)
2 Jessica da Silva Costa(jessica.costa.1@aluno.cefet-rj.br)
3 Jânio Lima(janio.lima@aluno.cefet-rj.br)
4 Lucas Tavares(lucas.giusti@aluno.cefet-rj.br)
5 Eduardo Bezerra(ebezerra@cefet-rj.br)
6 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)
7 Lais Baroni(lais.baroni}@eic.cefet-rj.br)
8 Esther Pacitti(esther.pacitti@lirmm.fr)
9 Fabio Porto (fporto@lncc.br)
10 Kele Belloze(kele.belloze@cefet-rj.br)
11 Eduardo Ogasawara( eogasawara@ieee.org)

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Reference
# Reference
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19 Lima, J., Tavares, L. G., Pacitti, E., Ferreira, J. E., Santos, I., Siqueira, I. G., Carvalho, D., Porto, F., Coutinho, R., and Ogasawara, E. (2024). Online Event Detection in Streaming Time Series: Novel Metrics and Practical Insights. In Proceedings of the International Joint Conference on Neural Networks, pages 1–8.
20 Moustati, I., Gherabi, N., and Saadi, M. (2024). Time-Series Forecasting Models for Smart Meters Data: An Empirical Comparison and Analysis. Journal Europeen des Systemes Automatises, 57(5):1419 – 1427.
21 Ogasawara, E., Castro, A., Mello, A., Paixão, E., Fraga, F., Lima, J., Souza, J., Baroni, L., Tavares, L., Borges, H., Salles, R., Carvalho, D., Bezerra, E., Coutinho, R., Pacitti, E., and Porto, F. (2024). harbinger: A Unified Time Series Event Detection Framework.
22 Ogasawara, E., Salles, R., Porto, F., and Pacitti, E. (2025). Event Detection in Time Series. Springer, 2025 edition.
23 Pang, G., Shen, C., Cao, L., and Van Den Hengel, A. (2021). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys, 54(2).
24 Paparrizos, J., Kang, Y., Boniol, P., Tsay, R. S., Palpanas, T., and Franklin, M. J. (2022). TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection. Proceedings of the VLDB Endowment, 15:1697 – 1711.
25 Ren, H., Xu, B., Wang, Y., Yi, C., Huang, C., Kou, X., Xing, T., Yang, M., Tong, J., and Zhang, Q. (2019). Time-series anomaly detection service at Microsoft. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 3009 – 3017.
26 Salles, R., Escobar, L., Baroni, L., Zorrilla, R., Ziviani, A., Kreischer, V., Delicato, F., Pires, P., Maia, L., Coutinho, R., Assis, L., and Ogasawara, E. (2020). Um framework para integração e análise de métodos de detecção de eventos em séries temporais. In Anais do Simpósio Brasileiro de Banco de Dados (SBBD). SBC.
27 Salles, R., Lima, J., Reis, M., Coutinho, R., Pacitti, E., Masseglia, F., Akbarinia, R., Chen, C., Garibaldi, J., Porto, F., and Ogasawara, E. (2024). SoftED: Metrics for soft evaluation of time series event detection. Computers and Industrial Engineering, 198.
28 Scharf, L. L. and Demeure, C. (1991). Statistical Signal Processing: Detection, Estimation, and Time Series Analysis. Addison-Wesley Publishing Company.
29 Souza, J., Pãixao, E., Fraga, F., Baroni, L., Alves, R. F. S., Belloze, K., Dos Santos, J., Bezerra, E., Porto, F., and Ogasawara, E. (2024). REMD: A Novel Hybrid Anomaly Detection Method Based on EMD and ARIMA. In Proceedings of the International Joint Conference on Neural Networks, pages 1–8.
30 Talagala, P. D., Hyndman, R. J., Smith-Miles, K., Kandanaarachchi, S., and Muñoz, M. A. (2020). Anomaly Detection in Streaming Nonstationary Temporal Data. Journal of Computational and Graphical Statistics, 29(1):13 – 27.
31 Truong, C., Oudre, L., and Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167.
32 Wenig, P., Schmidl, S., and Papenbrock, T. (2022). TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. Proceedings of the VLDB Endowment, 15(12):3678 – 3681.
33 Wu, R. and Keogh, E. J. (2023). Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress. IEEE Transactions on Knowledge and Data Engineering, 35(3):2421 – 2429.
34 Zhang, M., Guo, J., Li, X., and Jin, R. (2020). Data-driven anomaly detection approach for time-series streaming data. Sensors (Switzerland), 20(19):1 – 17.