AutoTiM - An Open-Source Service for Automated Provisioning and Operation of Time Series Based Machine Learning Models
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34107-6_21
Reference19 articles.
1. Bagnall, A., et al.: The UEA multivariate time series classification archive. arXiv preprint (2018). https://doi.org/10.48550/arXiv.1811.00075
2. Carmona, C.U., Aubet, F.X., Flunkert, V., Gasthaus, J.: Neural contextual anomaly detection for time series. In: IJCAI-2022, pp. 2843–2851 (2022). https://doi.org/10.24963/ijcai.2022/394
3. Carvalho, T.P., Soares, F.A., Vita, R., Francisco, R.P., Basto, J.P., Alcalá, S.G.: A systematic literature review of machine learning methods applied to predictive maintenance. CAIE 137, 106024 (2019). https://doi.org/10.1016/j.cie.2019.106024
4. Coelho, D., Costa, D., Rocha, E.M., Almeida, D., Santos, J.P.: Predictive maintenance on sensorized stamping presses by time series segmentation, anomaly detection, and classification algorithms. Procedia Comput. Sci. 200, 1184–1193 (2022). https://doi.org/10.1016/j.procs.2022.01.318
5. Davari, N., Veloso, B., de Assis Costa, G., Pereira, P.M., Ribeiro, R.P., Gama, J.: A survey on data-driven predictive maintenance for the railway industry. Sensors 21(17), 5739 (2021). https://doi.org/10.3390/s21175739
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