Leveraging Time Series Foundation Models to Detect Performance Anomalies in Software Systems

Anomaly detection is widely used in software performance engineering to identify performance degradations in production systems and to automatically trigger alerts. Over the years, several algorithms have been employed for this task, ranging from recurrent neural networks to traditional statistical models. Recently, a new class of algorithms, known as time series foundation models, has demonstrated remarkable effectiveness across a variety of time series analysis tasks. Despite these promising results, there is still limited understanding of how such models behave in the context of software anomaly detection. In this paper, we provide preliminary empirical insights into the effectiveness of two time series foundation models, Chronos and TSPulse, evaluated on two publicly available software anomaly detection datasets, AIOps and MSCloud. Our results show that foundation models can achieve comparable performance with respect to four representative TSAD baseline models, with the advantage of zero-shot prompting setup and the elimination of training stages and training data.