Anomaly Detection in Univariate Time Series Data in the Presence of Concept Drift

Abstract

Digital applications and devices record data over time to enable the users and managers to monitor their activity. Errors occur in data, including the time series data, for various reasons including software system failures and human errors. The problem of identifying errors, also referred to as anomaly detection, in time series data is a well studied topic by the data management and systems researchers. Such data are often recorded in dynamic environments where a change in the standard or the recording hardware can result in different and novel patterns arising in the data. Such novel patterns are caused by what is referred to as concept drifts. Concept drift occurs when there is a pattern change in the statistical properties of the data, e.g. the distribution of the data, over time. The problem of identifying anomalies in time series data recorded and stored in dynamic environments has not been extensively studied. In this study, we focus on this problem. We propose and implement a unified framework that is able to identify drifts in univariate time series data and incorporate information gained from the data to train a learning model that is able to detect anomalies in unseen univariate time series data.