Time series anomaly detection with ARIMA

Time series anomaly detection is an important task in application domains such as healthcare or manufacturing, among many others. Anomaly detection methods aim to identify observations that do not conform to the typical behavior of a dataset. In practice, anomalies can represent phenomena such as faults in machinery or fraudulent activity. Anomaly detection is a common task in machine learning (ML), and it has a few dedicated methods when it involves time series data. This type of dataset and the patterns therein can evolve over time, which complicates the modeling process and the effectiveness of the detectors. Statistical learning methods for time series anomaly detection problems usually follow a prediction-based approach or a reconstruction-based approach. In this recipe, we describe how to use an ARIMA method to create a prediction-based anomaly detection system for univariate time series.

Getting ready

We’ll focus on a univariate...