Anomaly Detection in Time Series: Uncovering the Potential of Forecasting in Industrial Context and Developing Insights into Anomalies in Hierarchically Aggregated Structures

Abstract

The proliferation of time series data across industrial domains has made anomaly detection a critical task for ensuring operational efficiency and reliability. This thesis explores two interconnected themes: the potential of forecasting models for Time Series Anomaly Detection (TSAD) in industrial settings and the unique challenges posed by Hierarchical Time Series (HTS), often found in industrial contexts. Leveraging probabilistic forecasting, the study examines the suitability of neural forecasting models as anomaly detectors, while also highlighting interpretability, scalability, and alignment with industrial requirements. Furthermore, the research identifies and formalizes novel anomaly types emerging from hierarchical aggregations, such as aggregation-concealed and perturbed anomalies, which current TSAD methods fail to address effectively. Empirical evaluations reveal that forecasting-based approaches while somewhat lag behind state-of-the-art TSAD methods on standard benchmarks, they excel in meeting industrial needs by offering advantages in interpretability, simplicity of deployment, and alignment with qualitative priorities. In the HTS context, results highlight the inadequacy of current methods for identifying hierarchical anomalies, emphasizing the need for tailored approaches. This work underscores the trade-off between accuracy and industrial applicability in anomaly detection, providing actionable insights for real-world adoption. Moreover, it lays the groundwork for future research around anomalies in HTS.