anomaly

A curated list of awesome anomaly detection resources

Kibana Alert & Report App for Elasticsearch

An open-source framework for real-time anomaly detection using Python, ElasticSearch and Kibana

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Anomaly detection

Tidy anomaly detection

Anomaly detection tutorial on univariate time series with an auto-encoder

Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Examples of not obvious behaviors for javascript beginner programmers

Official PyTorch code for WACV 2022 paper "CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows"

An Tensorflow Re-Implement of CVPR 2019 "Object-centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video"

Feature Space Singularity for Out-of-Distribution Detection. (SafeAI 2021)

2D Outlier Analysis using Shiny

CoRA Docs

NETS:Extremely Fast Outlier Detection from a Data Stream via Set-Based Processing

Anomaly Generator on Time Series

Ultrafast Local Outlier Detection from a Data Stream with Stationary Region Skipping

This tool parses log data and allows to define analysis pipelines for anomaly detection. It was designed to run the analysis with limited resources and lowest possible permissions to make it suitable for production server use.

Main component extraction for outlier detection

Thesis project about Unsupervised anomaly detection on the streaming time-series data of porfolio risk measures and returns.

[TF 2.x] PaDiM - unofficial tensorflow implementation of the paper 'a Patch Distribution Modeling Framework for Anomaly Detection and Localization'.

MTAD: Tools and Benchmark for Multivariate Time Series Anomaly Detection

Listen to the soothing sounds of attacks and anomalies detected by the Signal Sciences web protection platform.

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.

Anomaly Detection In An IoT-Acquired Environmental Sensor Data