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English Information

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Authors
# Name
1 Anthony Heimlich(anthony.heimlich@aluno.cefet-rj.br)
2 Gabriel Giuliano(gabriel.giuliano@aluno.cefet-rj.br)
3 Renato Mauro(renato.mauro@cefet-rj.br)
4 Antonio Castro Filho(antonio.castro.filho@aluno.cefet-rj.br)
5 Helga Balbi(helga.balbi@cefet-rj.br)
6 Rafaelli Coutinho(rafaelli.coutinho@cefet-rj.br)
7 Jânio Lima(janio.lima@aluno.cefet-rj.br)
8 Eduardo Ogasawara(eogasawara@ieee.org)

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Reference
# Reference
1 Cao, Z., Seeuws, N., De Vos, M., e Bertrand, A. (2024). Change Point Detection in Multi-Channel Time Series via a Time-Invariant Representation. IEEE Transactions on Knowledge and Data Engineering, 36:7743 – 7756.
2 Corizzo, R., Baron, M., e Japkowicz, N. (2022). CPDGA: Change point driven growing auto-encoder for lifelong anomaly detection. Knowledge-Based Systems, 247:108756.
3 De Ryck, T., De Vos, M., e Bertrand, A. (2021). Change Point Detection in Time Series Data Using Autoencoders with a Time-Invariant Representation. IEEE Transactions on Signal Processing, 69:3513 – 3524.
4 Gupta, M., Wadhvani, R., e Rasool, A. (2022). Real-time Change-Point Detection: A deep neural network-based adaptive approach for detecting changes in multivariate time series data. Expert Systems with Applications, 209:118260.
5 Li, J., Fearnhead, P., Fryzlewicz, P., e Wang, T. (2024). Automatic change-point detection in time series via deep learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 86:273–285.
6 Lima, J., Castro, H., Oliveira, L., Paixão, E., Baroni, L., Salles, R., Vargas, R., e Ogasawara, E. (2025). UniTED: A Unified Time Series Event Detection Repository. In Brazilian e-Science Workshop (BreSci), pages 1–8. SBC.
7 Ogasawara, E., Salles, R., Porto, F., e Pacitti, E. (2025). Event Detection in Time Series. Synthesis Lectures on Data Management. Springer Nature Switzerland, Cham, 1 edition.
8 Salles, R., Escobar, L., Baroni, L., Zorrilla, R., Ziviani, A., Kreischer, V., Delicato, F., Pires, P. F., Maia, L., Coutinho, R., Assis, L., e Ogasawara, E. (2020). Harbinger: 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), pages 73–84. SBC.
9 Salles, R., Lima, J., Reis, M., Coutinho, R., Pacitti, E., Masseglia, F., Akbarinia, R., Chen, C., Garibaldi, J., Porto, F., e Ogasawara, E. (2024). SoftED: Metrics for soft evaluation of time series event detection. Computers and Industrial Engineering, 198.
10 Vargas, R. E. V., Munaro, C. J., Ciarelli, P. M., Medeiros, A. G., Amaral, B. G. d., Barrionuevo, D. C., Araújo, J. C. D. d., Ribeiro, J. L., e Magalhães, L. P. (2019). A realistic and public dataset with rare undesirable real events in oil wells. Journal of Petroleum Science and Engineering, 181.
11 Ygorra, B., Frappart, F., Wigneron, J., Moisy, C., Catry, T., Baup, F., Hamunyela, E., e Riazanoff, S. (2021). Monitoring loss of tropical forest cover from Sentinel-1 timeseries: A CuSum-based approach. International Journal of Applied Earth Observation and Geoinformation, 103:102532.
12 Ygorra, B., Frappart, F., Wigneron, J.-P., Catry, T., Pillot, B., Pfefer, A., Courtalon, J., e Riazanoff, S. (2024). A near-real-time tropical deforestation monitoring algorithm based on the CuSum change detection method. Frontiers in Remote Sensing, 5.