Prediction, Event and Anomaly
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Anomaly Detection Software
||Skyline is a real-time anomaly detection system, built to enable passive monitoring of hundreds of thousands of metrics
||Luminol is a light weight python library for time series data analysis. The two major functionalities it supports are anomaly detection and correlation. It can be used to investigate possible causes of anomaly.
||An open-source framework for real-time anomaly detection using Python, Elasticsearch and Kibana.
This section includes some time-series software for anomaly detection-related tasks, such as forecasting.
||Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays.
||The library has a good array of modern time series models, as well as a flexible array of inference options (frequentist and Bayesian) that can be applied to these models.
||Porting of R’s auto.arima with a scikit-learn-friendly interface.
||General implementation of SAX, as well as HOTSAX for anomaly detection.
- Numenta’s NAB
– NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications.
- Yahoo’s Webscope S5
– The dataset consists of real and synthetic time-series with tagged anomaly points. The dataset tests the detection accuracy of various anomaly-types including outliers and change-points.
Time Series Anomaly Detection
classification, detection, anomaly, event, time-series, deep, keras, LTSM
- HOT SAX: Finding the Most Unusual Time Series Subsequence: Algorithms and Applications, Eamonn Keogh, Jessica Lin, Ada Fu, 2005 - Paper, Materials
- LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection, Pankaj Malhotra, Anusha Ramakrishnan, Gaurangi Anand, Lovekesh Vig, Puneet Agarwal, Gautam Shroff, 2016 - Paper
- Credit Card Transactions, Fraud Detection, and Machine Learning: Modelling Time with LSTM Recurrent Neural Networks, Bénard Wiese and Christian Omlin, 2009 - Springer
- Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection, Jihyun Kim, Jaehyun Kim, Huong Le Thi Thu, and Howon Kim - Paper
- Deep Recurrent Neural Network-based Autoencoders for Acoustic Novelty Detection, Erik Marchi Fabio Vesperini, Stefano Squartini, and Bjo ̈rn Schuller - Paper
- A Novel Approach for Automatic Acoustic Novelty Detection Using a Denoising Autoencoder with Bidirectional LSTM Neural Networks, Erik Marchi, Fabio Vesperini, Florian Eyben, Stefano Squartini, Bjo ̈rn Schuller - Paper
* Transfer learning
- Transfer Representation-Learning for Anomaly Detection, Jerone T. A. Andrews, Thomas Tanay, Edward J. Morton, Lewis D. Griffin, 2016 - Paper
* Anomaly Detection Based on Sensor Data in Petroleum Industry Applications, Luis Martí,1, Nayat Sanchez-Pi, José Manuel Molina, and Ana Cristina Bicharra Garcia - Paper
* Anomaly detection in aircraft data using recurrent nueral networks (RNN), Anvardh Nanduri, Lance Sherry - Paper
* Bayesian Online Changepoint Detection, Ryan Prescott Adams, David J.C. MacKay - Paper
* Anomaly Detection in Aviation Data using Extreme Learning Machines, Vijay Manikandan Janakiraman, David Nielsen - Paper