ICAD: A Self-Supervised Autoregressive Approach for Multi-Context Anomaly Detection in Human Mobility Data

Abnormal human mobility patterns often signal disruptions, emergencies, or health-related risks, making their detection critical for applications in public safety, urban monitoring, and healthcare. Existing approaches for human mobility anomaly detection typically focus on either identifying visits to unusual places or overall deviations from individual- and population-level norms at the agent-level. However, these methods often (1) overlook fine-grained temporal anomalies, and (2) lack interpretability, as they do not reveal which specific spatiotemporal components of a visit contribute to its anomalous nature. To overcome these limitations, we present ICAD (Interpretable Component-wise Anomaly Detection), a self-supervised autoregressive model that detects both spatial and temporal anomalies by modeling deviations in an individual's visit-level mobility behavior. ICAD is trained on normal visit sequences using a next-visit prediction objective to learn the distribution of visits under regular conditions. At inference, it computes component-wise anomaly scores for each visit by measuring relative divergence from the learned distribution of normal behavior. Specifically, ICAD proposes a top-k deviation metric for discrete spatial anomalies and introduces a novel relative mode-based scoring function for detecting temporal anomalies in continuous time. Experiments on a large scale synthetic human mobility dataset show that ICAD outperforms prior methods in both visit-level and agent-level anomaly detection. For reproducability purposes, the source code is accessible at https://github.com/USC-InfoLab/ICAD.