435 papers with code • 19 benchmarks • 32 datasets
Anomaly Detection, Anomaly Segmentation, Novelty Detection, Out-of-Distribution Detection
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data.
We present a Reverse Reinforcement Learning (Reverse RL) approach for representing retrospective knowledge.
This work proposes a novel method to robustly and accurately model time series with heavy-tailed noise, in non-stationary scenarios.
We present results and analysis for a wide range of algorithms on this benchmark, and discuss future challenges for the emerging field of streaming analytics.
Ranked #1 on Anomaly Detection on Numenta Anomaly Benchmark