Can Large Language Models be Anomaly Detectors for Time Series?
Sarah Alnegheimish, Linh Nguyen, Laure Berti-Equille, Kalyan Veeramachaneni
DSAA
IEEE International Conference on Data Science and Advanced Analytics, 2024.
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OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User
Sarah Alnegheimish, Laure Berti-Equille, Kalyan Veeramachaneni
BigData
IEEE International Conference on Big Data, 2024.
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AER: Auto-Encoder with Regression for Time Series Anomaly Detection
Lawrence Wong, Dongyu Liu, Laure Berti-Equille, Sarah Alnegheimish, Kalyan Veeramachaneni
BigData
IEEE International Conference on BigData, 2022.
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Sintel: A Machine Learning Framework to Extract Insights from Signals
Sarah Alnegheimish, Dongyu Liu, Carles Sala, Laure Berti-Équille, Kalyan Veeramachaneni
SIGMOD
ACM SIGMOD/PODS International Conference on Management of Data, 2022
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MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series
Dongyu Liu, Sarah Alnegheimish, Alexandra Zytek, Kalyan Veeramachaneni
CSCW
The ACM Conference on Computer Supported Cooperative Work, 2022
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[video]
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks
Alexander Geiger, Dongyu Liu, Sarah Alnegheimish, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
BigData
IEEE International Conference on BigData, 2020
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