Recent advances in technology have brought major breakthroughs in data collection,
enabling a large amount of data to be gathered over time and thus generating time series …

Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

Abstract Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …

Testing for outliers with conformal p-values Page 1 The Annals of Statistics 2023, Vol. 51, No.
1, 149–178 https://doi.org/10.1214/22-AOS2244 © Institute of Mathematical Statistics, 2023 …

We develop a method to construct distribution-free prediction intervals for dynamic time-
series, called\Verb| EnbPI| that wraps around any bootstrap ensemble estimator to construct …

Real-time diagnostics of complex technical systems such as power plants are critical to keep
the system in its working state. An ideal diagnostic system must detect any fault in advance …

In response to the growing importance of geospatial data, its analysis including semantic
segmentation becomes an increasingly popular task in computer vision today. Convolutional …

To ensure the stable long-time operation of satellites, evaluate the satellite status, and
improve satellite maintenance efficiency, we propose an anomaly detection method based …

This paper develops novel conformal methods to test whether a new observation was
sampled from the same distribution as a reference set. Blending inductive and transductive …

PySAD is an open-source python framework for anomaly detection on streaming data.
PySAD serves various state-of-the-art methods for streaming anomaly detection. The …