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In this project, we will develop and explore the use of representation learning methods, e.g. deep learning-based approaches (including time series embedding methods), that can capture and encode key characteristics of time series data for anomaly detection. Methods that are inherently explainable (e.g. causal relations learned via causal inferences), can be learned in an incremental setting (e.g. online learning with tree-based approaches) or computationally efficient (e.g. echo state network via reservoir computing), are of great interest as well. The developed approach will be evaluated and compared with a few time series embedding methods on a real-world dataset collected from EVs. This project is a collaboration with Volo, you will work closely with the Advanced Analytics Team at Volvo Group Technology. Please contact Yuantao for more details.
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