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Detecting Relative Anomaly

arXiv.org Machine Learning

System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly. We propose a novel concept for anomaly detection, called relative anomaly detection. It is tailored to be robust towards anomalies that occur frequently, by taking into account their location relative to the most typical observations. The approaches we develop are computationally feasible even for large data sets, and they allow real-time detection. We illustrate using data sets of potential scraping attempts and Wi-Fi channel utilization, both from Google, Inc.


A closer look at time series anomaly detection Anodot

#artificialintelligence

Simple enough to be embedded in text as a sparkline, but able to speak volumes about your business, time series data is the basic input of Anodot's automated anomaly detection system. This article begins our three-part series in which we take a closer look at the specific techniques Anodot uses to extract insights from your data.


Group Anomaly Detection using Flexible Genre Models

Neural Information Processing Systems

An important task in exploring and analyzing real-world data sets is to detect unusual and interesting phenomena. In this paper, we study the group anomaly detection problem. Unlike traditional anomaly detection research that focuses on data points, our goal is to discover anomalous aggregated behaviors of groups of points. For this purpose, we propose the Flexible Genre Model (FGM). FGM is designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies. We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.


Numenta Anomaly Benchmark (NAB) Competition • /r/MachineLearning

#artificialintelligence

The Numenta Anomaly Benchmark (NAB) is an open-source dataset and scoring methodology designed for evaluating anomaly detection algorithms for real-world streaming analytics. Entries are due by July 1st. We designed NAB to be useful for the research community; everything is open-source and it's easy to run your anomaly detection algorithms on NAB (in any language).


Group Anomaly Detection using Flexible Genre Models

Neural Information Processing Systems

An important task in exploring and analyzing real-world data sets is to detect unusual and interesting phenomena. In this paper, we study the group anomaly detection problem. Unlike traditional anomaly detection research that focuses on data points, our goal is to discover anomalous aggregated behaviors of groups of points. For this purpose, we propose the Flexible Genre Model (FGM). FGM is designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies.