PATH: A discrete-sequence dataset for evaluating online unsupervised anomaly detection approaches for multivariate time series.

Lucas Correia

Lucas Correia Jan-Christoph Goos Thomas Bäck Anna V. Kononova PATH: A discrete-sequence dataset for evaluating online unsupervised anomaly detection approaches for multivariate time series. 100573 2025 42 Big Data Res. https://doi.org/10.1016/j.bdr.2025.100573 db/journals/bdr/bdr42.html#CorreiaGBK25 streams/journals/bdr

Lucas Correia Jan-Christoph Goos Philipp Klein Thomas Bäck Anna V. Kononova Corrigendum to "Online model-based anomaly detection in multivariate time series: Taxonomy, survey, research challenges and future directions" [Eng. Appl. Artif. Intell. 138 (2024) 109323]. 110170 2025 145 Eng. Appl. Artif. Intell. https://doi.org/10.1016/j.engappai.2025.110170 db/journals/eaai/eaai145.html#CorreiaGKBK25 streams/journals/eaai

Lucas Correia Jan-Christoph Goos Thomas Bäck Anna V. Kononova A Dataset for Evaluating Online Anomaly Detection Approaches for Discrete Multivariate Time Series. 2025 https://doi.org/10.5281/zenodo.14892756 Zenodo streams/repo/zenodo data/11/CorreiaGBK24a

Lucas Correia Jan-Christoph Goos Thomas Bäck Anna V. Kononova DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches. 2025 September abs/2509.05663 CoRR https://doi.org/10.48550/arXiv.2509.05663 db/journals/corr/corr2509.html#abs-2509-05663 streams/journals/corr

Lucas Correia Jan-Christoph Goos Philipp Klein Thomas Bäck Anna V. Kononova Online model-based anomaly detection in multivariate time series: Taxonomy, survey, research challenges and future directions. 109323 2024 138 Eng. Appl. Artif. Intell. https://doi.org/10.1016/j.engappai.2024.109323 db/journals/eaai/eaai138.html#CorreiaGKBK24 streams/journals/eaai

Lucas Correia Jan-Christoph Goos Thomas Bäck Anna V. Kononova A Dataset for Evaluating Online Anomaly Detection Approaches for Discrete Multivariate Time Series. 2024 https://doi.org/10.5281/zenodo.13255121 Zenodo streams/repo/zenodo data/11/CorreiaGBK24a

Lucas Correia Jan-Christoph Goos Philipp Klein Thomas Bäck Anna V. Kononova TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data. 2024 abs/2407.06849 CoRR https://doi.org/10.48550/arXiv.2407.06849 db/journals/corr/corr2407.html#abs-2407-06849 streams/journals/corr

Lucas Correia Jan-Christoph Goos Philipp Klein Thomas Bäck Anna V. Kononova Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions. 2024 abs/2408.03747 CoRR https://doi.org/10.48550/arXiv.2408.03747 db/journals/corr/corr2408.html#abs-2408-03747 streams/journals/corr

Lucas Correia Jan-Christoph Goos Thomas Bäck Anna V. Kononova A Dataset for Evaluating Online Anomaly Detection Approaches for Discrete Multivariate Time Series. 2024 abs/2411.13951 CoRR https://doi.org/10.48550/arXiv.2411.13951 db/journals/corr/corr2411.html#abs-2411-13951 streams/journals/corr

Lucas Correia Jan-Christoph Goos Philipp Klein Thomas Bäck Anna V. Kononova MA-VAE: Multi-Head Attention-Based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-Series Applied to Automotive Endurance Powertrain Testing. 407-418 2023 IJCCI https://doi.org/10.5220/0012163100003595 conf/ijcci/2023 db/conf/ijcci/ijcci2023.html#CorreiaGKBK23

Lucas Correia Jan-Christoph Goos Philipp Klein Thomas Bäck Anna V. Kononova MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing. 2023 abs/2309.02253 CoRR https://doi.org/10.48550/arXiv.2309.02253 db/journals/corr/corr2309.html#abs-2309-02253

Thomas Bäck Jan-Christoph Goos Philipp Klein Anna V. Kononova