Unsupervised Model Selection for Time Series Anomaly Detection (ICLR 2023) [paper]

TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis (ICLR 2023) [paper]

Detecting Multivariate Time Series Anomalies with Zero Known Label (AAAI 2023) [paper]

Towards a rigorous evaluation of time-series anomaly detection (AAAI 2022) [paper]

Ts2vec: Towards universal representation of time series (AAAI 2022) [paper]

Deep variational graph convolutional recurrent network for multivariate time series anomaly detection (ICML 2022) [paper]

Rethinking graph neural networks for anomaly detection (ICML 2022) [paper]

GRELEN: Multivariate time series anomaly detection from the perspective of graph relational learning (IJCAI 2022) [paper]

Neural Contextual Anomaly Detection for Time Series (IJCAI 2022) [paper]

Time Series Anomaly Detection with Association Discrepancy (ICLR 2022) [paper]

Graph-augmented normalizing flows for anomaly detection of multiple time series (ICLR 2022) [paper]

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series (AAAI 2021) [paper]

Time Series Anomaly Detection with Multiresolution Ensemble Decoding (AAAI 2021) [paper]

Neural Transformation Learning for Deep Anomaly Detection Beyond Images (ICML 2021) [paper]

Conformal prediction interval for dynamic time-series (ICML 2021) [paper]

Timeseries anomaly detection using temporal hierarchical one-class network (NeurIPS 2020) [paper]

mbd.pub/o/GeBENHAGEN

擅长现代信号处理(改进小波分析系列，改进变分模态分解，改进经验小波变换，改进辛几何模态分解等等)，改进机器学习，改进深度学习，机械故障诊断，改进时间序列分析(金融信号，心电信号，振动信号等)