paubric

paubric / AnomalyDetection / 0.1.0

README.md

Overview

Anomaly detection is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.

Applicable Scenarios and Problems

Anomaly detection is applicable in a variety of domains, such as intrusion detection, fraud detection, fault detection, system health monitoring, event detection in sensor networks, and detecting ecosystem disturbances.

Usage

Input

ParameterDescription
dataArray of arrays with the same shape

Output

ParameterDescription
labelsCorresponding mask for each input array: 1 for no anomaly, -1 for anomaly

Examples

*Input: * {"data":[[1, 2], [2, 1], [1, 2], [-3, 7]]}

*Output: * { "labels": [ -1, -1, -1, 1 ] }