###### Abstract  
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly-type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types.  
anomaly detection; time series; machine learning; deep learning; benchmark +
Footnote †: journal: Medical  
2. \"What different types of anomalies are the methods capable of detecting?\"  
To address these questions, we compare the classical methods of Robust Random Cut Forest (RRCF) [9], Maximally Divergent Intervals (MDI) [10] and MERLIN [11] to the deep learning methods of Autoencoder (AE), Graph Augmented Normalizing Flows (GANF) [12], and Transformer Networks for Anomaly Detection (TranAD) [13]. We evaluate these methods on the UCR Anomaly Archive [14], a new benchmark dataset for time series anomaly detection. This archive consists of 250 univariate time series from four domains: human medicine, industry, biology and meteorology. To ensure a fair comparison, we carefully design our experimental setup and perform intensive hyperparameter tuning for applicable methods. To the best of our knowledge, this is the first work to conduct an experimental comparison of classical and deep-learning methods for anomaly detection in time series. Our key contributions are:  
* We conduct a comprehensive comparison of six state-of-the-art anomaly detection methods for time series data using the UCR Anomaly Archive benchmark dataset. Our comparison is carried out in a well-defined and fair benchmark environment.
* We enhance the UCR Anomaly Archive by annotating it with 16 distinct anomaly types, providing a more nuanced and informative benchmark.
* We address two crucial questions in the field of anomaly detection: (1) whether the superior performance of deep-learning methods justifies the loss of interpretability of traditional methods and (2) the similarities and differences between the analyzed methods in terms of detecting different anomaly types.
* We examine the impact of subsequence length on the performance of the MDI and MERLIN methods, and compare point-wise to subsequence-wise features for the RRCF method.  
The remainder of this paper is organized as follows: after providing an introduction to time series data and different types of anomalies in Sections 1.1 and 1.2, respectively, we present related work in Section 1.3. In Section 2.1, we present the six anomaly detection methods, followed by a description of the UCR Anomaly Archive dataset in Section 2.2 and the experimental setup in Section 2.3. The results of our experiments are presented in Section 3 and discussed in Section 4. Finally, we summarize our findings and provide an outlook on future work in Section 5.