anomaly : Detection of Anomalous Structure in Time Series Data

TL;DR

This paper introduces the anomaly package, which implements advanced algorithms for detecting both point and collective anomalies in time series data, demonstrated through simulated and real-world examples.

Contribution

It provides a comprehensive implementation of recent collective and point anomaly detection algorithms within an accessible software package.

Findings

Effective detection of anomalies demonstrated on simulated data.

Successful application to real-world time series data.

Provides users with multiple detection method options.

Abstract

One of the contemporary challenges in anomaly detection is the ability to detect, and differentiate between, both point and collective anomalies within a data sequence or time series. The anomaly package has been developed to provide users with a choice of anomaly detection methods and, in particular, provides an implementation of the recently proposed Collective And Point Anomaly family of anomaly detection algorithms. This article describes the methods implemented whilst also highlighting their application to simulated data as well as real data examples contained in the package.