Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series

The detection of anomalies, or observations that significantly deviate from what is considered normal [1], in time series data is essential in various fields, including healthcare [2], cybersecurity [3, 4], industry [5], and robotics [6]. Anomaly detection is a notoriously challenging task, as the definition of what is considered anomalous can vary based on the context or application [7]. Moreover, the absence of labeled training data for non-academic problems often precludes the use of supervised machine learning techniques. Anomaly detection in data streams, which requires rapid results while aiming to detect anomalies accurately and efficiently, is frequently necessary. It is important to minimize false positive detections to prevent alarm fatigue, which can result in a serious problem being overlooked due to excessive false alarms [7]. It is also necessary to choose the appropriate method based on the application and, often, domain knowledge, as the existence of a universal anomaly detection method is a myth [8]. Choosing the appropriate method from the plethora of available options can be a challenge in itself, as different methods have different strengths in detecting certain types of anomalies. The numerous available methods can be categorized using various criteria, such as the underlying probabilistic, classification, or reconstruction-based model [1], the type of input data (univariate or multivariate), the need for labeled training data, or the ability to process data streams. In this work, we compare six unsupervised anomaly detection methods with varying complexities.