6 papers
When lookout sees crackle: Anomaly detection via kernel density estimation
Rob J Hyndman, Sevvandi Kandanaarachchi, Katharine Turner
We present an updated version of lookout -- an algorithm for detecting anomalies using kernel density estimates with bandwidth based on Rips death diameters -- with theoretical gua…
Anomaly detection using surprisals
Rob J Hyndman, David T. Frazier
Anomaly detection methods are widely used but often rely on ad hoc rules or strong assumptions, and they often focus on tail events, missing ``inlier'' anomalies that occur in low-…
Online conformal inference for multi-step time series forecasting
Xiaoqian Wang, Rob J Hyndman
We consider the problem of constructing distribution-free prediction intervals for multi-step time series forecasting, with a focus on the temporal dependencies inherent in multi-s…
ggtime: A Grammar of Temporal Graphics
Cynthia A. Huang, Mitchell O'Hara-Wild, Rob J. Hyndman +1
Visualizing changes over time is fundamental to learning from the past and anticipating the future. However, temporal semantics can be complicated, and existing visualization tools…
Good intentions, unintended consequences: exploring forecasting harms
Bahman Rostami-Tabar, Travis Greene, Galit Shmueli +1
Organizations worldwide that rely on data-driven approaches regularly employ forecasting methods to enhance their planning and decision-making processes. While extensive research h…
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs
Sevvandi Kandanaarachchi, Conrad Sanderson, Rob J. Hyndman
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existi…