38 citations · 60 across the 10 of their papers we have counts for
8 papers
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series
Mostafa Rahmani, Anoop Deoras, Laurent Callot
This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based…
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection
Cristian Challu, Peihong Jiang, Ying Nian Wu +1
Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets.…
Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies
Lenon Minorics, Caner Turkmen, David Kernert +3
This paper proposes a new approach for testing Granger non-causality on panel data. Instead of aggregating panel member statistics, we aggregate their corresponding p-values and sh…
Online Time Series Anomaly Detection with State Space Gaussian Processes
Christian Bock, François-Xavier Aubet, Jan Gasthaus +3
We propose r-ssGPFA, an unsupervised online anomaly detection model for uni- and multivariate time series building on the efficient state space formulation of Gaussian processes. F…
Improve black-box sequential anomaly detector relevancy with limited user feedback
Luyang Kong, Lifan Chen, Ming Chen +2
Anomaly detectors are often designed to catch statistical anomalies. End-users typically do not have interest in all of the detected outliers, but only those relevant to their appl…
A simple and effective predictive resource scaling heuristic for large-scale cloud applications
Valentin Flunkert, Quentin Rebjock, Joel Castellon +2
We propose a simple yet effective policy for the predictive auto-scaling of horizontally scalable applications running in cloud environments, where compute resources can only be ad…