aqibsaeed/AnomalyDetection

#artificialintelligence

Anomaly detection is the problem of identifying data points that don't conform to expected (normal) behaviour. Unexpected data points are also known as outliers and exceptions etc. Anomaly detection has crucial significance in the wide variety of domains as it provides critical and actionable information. For example, an anomaly in MRI image scan could be an indication of the malignant tumor or anomalous reading from production plant sensor may indicate faulty component. This repository provide an anomaly detection algortihm based on estimation of gaussian distribution. This is a Python implementation of algorithm discussed by Andrew Ng in his course of Machine Learning on Coursera.


twitter/AnomalyDetection

#artificialintelligence

AnomalyDetection is an open-source R package to detect anomalies which is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. The AnomalyDetection package can be used in wide variety of contexts. The underlying algorithm – referred to as Seasonal Hybrid ESD (S-H-ESD) builds upon the Generalized ESD test for detecting anomalies. Note that S-H-ESD can be used to detect both global as well as local anomalies. This is achieved by employing time series decomposition and using robust statistical metrics, viz., median together with ESD.



2d5dZnf

#artificialintelligence

Some of the popular anomaly detection techniques are Density-based techniques (k-nearest neighbor,local outlier factor,Subspace and correlation-based, outlier detection, One class support vector machines, Replicator neural networks, Cluster analysis-based outlier detection, Deviations from association rules and frequent itemsets, Fuzzy logic based outlier detection and Ensemble techniques. RapidMiner provides an integrated environment for machine learning, data mining, text mining, predictive analytics and business analytics. Scikit-learn is an open source machine learning library for the Python programming language.It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. You may also live to read, Top Business Intelligence companies, Open Source and Free Business Intelligence Solutions, Cloud – SaaS – OnDemand Business Intelligence Solutions, Top Free Extract, Transform, and Load, ETL Software, Freemium Cloud Business Intelligence Solutions, Top Embedded Analytics Business Intelligence Software, Top Dashboard Software, and Top Data Visualization Software.


Statistical Anomaly Detection for Train Fleets

AAAI Conferences

We have developed a method for statistical anomaly detection which has been deployed in a tool for condition monitoring of train fleets. The tool is currently used by several railway operators over the world to inspect and visualize the occurrence of event messages generated on the trains. The anomaly detection component helps the operators to quickly find significant deviations from normal behavior and to detect early indications for possible problems. The savings in maintenance costs comes mainly from avoiding costly breakdowns, and have been estimated to several million Euros per year for the tool. In the long run, it is expected that maintenance costs can be reduced with between 5 and 10 % by using the tool.