Reconstruction-based unsupervised drift detection over multivariate streaming data. - Daniil Kaminskyi; Bin Li 089; Emmanuel Müller (ICDM (Workshops), 2022)

ADEPT: Anomaly Detection, Explanation and Processing for Time Series with a Focus on Energy Consumption Data. - Benedikt Tobias Müller; Marvin Ender; Jan Erik Swiadek; Mengcheng Jin; Simon Winkel; Dominik Niedziela; Bin Li 089; Jelle Hüntelmann; Emmanuel Müller (ECML/PKDD (6), 2022)

STAD: State-Transition-Aware Anomaly Detection Under Concept Drifts. - Bin Li 089; Emmanuel Müller (OLUD@WCCI, 2022)

On Model Performance Estimation in Time Series Anomaly Detection. - Arn Baudzus; Bin Li 089; Adnane Jadid; Emmanuel Müller (CIIS, 2023)

State-Transition-Aware Anomaly Detection Under Concept Drifts. - Bin Li 089; Emmanuel Müller (DaWaK, 2023)

slidSHAPs - sliding Shapley Values for correlation-based change detection in time series. - Chiara Balestra; Bin Li 089; Emmanuel Müller (DSAA, 2023)

Contrastive Time Series Anomaly Detection by Temporal Transformations. - Bin Li 089; Emmanuel Müller (IJCNN, 2023)

The Good, The Bad, and The Average: Benchmarking of Reconstruction Based Multivariate Time Series Anomaly Detection. - Arn Baudzus; Bin Li 089; Adnane Jadid; Emmanuel Müller (ECML/PKDD (7), 2023)

Prototypes as Explanation for Time Series Anomaly Detection. - Bin Li 089; Carsten Jentsch; Emmanuel Müller (CoRR, 2023)

On the Consistency and Robustness of Saliency Explanations for Time Series Classification. - Chiara Balestra; Bin Li 089; Emmanuel Müller (CoRR, 2023)

Cohesive Explanation for Time Series Prediction. - Bin Li 089; Emmanuel Müller (IJCNN, 2024)

State-transition-aware anomaly detection under concept drifts. - Bin Li 089; Shubham Gupta; Emmanuel Müller (Data Knowl. Eng., 2024)

Unsupervised temporal anomaly detection: Time series, data stream, and interpretability. - Bin Li 089 (2024)