OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data

TL;DR

OML-AD, a novel approach for anomaly detection (AD) based on online machine learning (OML), is proposed and it is shown that it outperforms state-of-the-art baseline methods in terms of accuracy and computational efficiency.

Abstract

Time series are ubiquitous and occur naturally in a variety of applications -- from data recorded by sensors in manufacturing processes, over financial data streams to climate data. Different tasks arise, such as regression, classification or segmentation of the time series. However, to reliably solve these challenges, it is important to filter out abnormal observations that deviate from the usual behavior of the time series. While many anomaly detection methods exist for independent data and stationary time series, these methods are not applicable to non-stationary time series. To allow for non-stationarity in the data, while simultaneously detecting anomalies, we propose OML-AD, a novel approach for anomaly detection (AD) based on online machine learning (OML). We provide an implementation of OML-AD within the Python library River and show that it outperforms state-of-the-art baseline methods in terms of accuracy and computational efficiency.

Figures (4)

• Figure 1: Anomaly Types in Time Series Data
• Figure 2: Weekly Temperature Data with Synthesized Anomalies
• Figure 3: Forecast on CPU Utilization Data. Top: OML-AD. Bottom: SARIMA without Retraining
• Figure 4: Error of Forecasts on CPU Utilization Data. Top: OML-AD Bottom: SARIMA