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English Information

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Authors
# Name
1 Eduardo Ogasawara( eogasawara@ieee.org)

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Reference
# Reference
1 Ahmad, S., Lavin, A., Purdy, S., e Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262:134 – 147.
2 Jensen, S. K., Pedersen, T. B., e Thomsen, C. (2017). Time Series Management Systems: A Survey. IEEE Transactions on Knowledge and Data Engineering, 29(11):2581 – 2600.
3 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.
4 Olteanu, M., Rossi, F., e Yger, F. (2023). Meta-survey on outlier and anomaly detection. Neurocomputing, 555.
5 Pang, G., Shen, C., Cao, L., e Van Den Hengel, A. (2021). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys, 54(2).
6 Paparrizos, J., Boniol, P., Palpanas, T., Tsay, R. S., Elmore, A., e Franklin, M. J. (2022). Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection. Proceedings of the VLDB Endowment, 15(11):2774 – 2787.
7 Salles, R., Lima, J., Coutinho, R., Pacitti, E., Masseglia, F., Akbarinia, R., Chen, C., Garibaldi, J., Porto, F., e Ogasawara, E. (2023). SoftED: Metrics for Soft Evaluation of Time Series Event Detection.
8 Schmidl, S., Wenig, P., e Papenbrock, T. (2022). Anomaly Detection in Time Series: A Comprehensive Evaluation. Proceedings of the VLDB Endowment, 15(9):1779 – 1797.
9 Tatbul, N., Lee, T. J., Zdonik, S., Alam, M., e Gottschlich, J. (2018). Precision and recall for time series. In Advances in Neural Information Processing Systems, volume 2018-December, pages 1920 – 1930.
10 Truong, C., Oudre, L., e Vayatis, N. (2020). Selective review of offline change point detection methods. Signal Processing, 167.
11 Wenig, P., Schmidl, S., e Papenbrock, T. (2022). TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. Proceedings of the VLDB Endowment, 15(12):3678 – 3681.
12 Zhao, L. (2021). Event Prediction in the Big Data Era: A Systematic Survey. ACM Computing Surveys, 54(5).