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Time series anomaly detection for Heavy-duty vehicles (in collaboration with Volvo)
OneLineSummary Detecting anomalies in multivariate time series data via learned representations  +
References - Carvalho, J., Zhang, M., Geyer, R., Cotr- Carvalho, J., Zhang, M., Geyer, R., Cotrini, C., & Buhmann, J. M. (2024). Invariant anomaly detection under distribution shifts: a causal perspective. Advances in Neural Information Processing Systems, 36. - Wang, Z., & Veitch, V. (2022). A unified causal view of domain invariant representation learning. -Ikonomovska, E., Gama, J., & Džeroski, S. (2015). Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing, 150, 458-470. - Muallem, A., Shetty, S., Pan, J. W., Zhao, J., & Biswal, B. (2017). Hoeffding tree algorithms for anomaly detection in streaming datasets: A survey. Journal of Information Security, 8(4). - Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016, July). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1). - Gallicchio, C. (2024). Euler state networks: Non-dissipative reservoir computing. Neurocomputing, 579, 127411. - Foumani, N. M., Tan, C. W., Webb, G. I., Rezatofighi, H., & Salehi, M. (2024). Series2vec: similarity-based self-supervised representation learning for time series classification. Data Mining and Knowledge Discovery, 1-25. - Baevski, A., Hsu, W. N., Xu, Q., Babu, A., Gu, J., & Auli, M. (2022, June). Data2vec: A general framework for self-supervised learning in speech, vision and language. In International Conference on Machine Learning (pp. 1298-1312). PMLR.on Machine Learning (pp. 1298-1312). PMLR.
StudentProjectStatus Open  +
Supervisors Yuantao & TBD +
Title Time series anomaly detection for Heavy-duty vehicles (in collaboration with Volvo)  +
Categories StudentProject  +
Modification dateThis property is a special property in this wiki. 21 October 2024 14:00:40  +
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