The central challenge in temporal data analysis is to obtain knowledge about its underlying dynamics. In this paper, we address the observation of noisy, stochastic processes and attempt to detect temporal segments that are related to inconsistencies and irregularities in its dynamics. Many conventional anomaly detection approaches detect anomalies based on the distance between patterns, and often provide only limited intuition about the generative process of the anomalies. Meanwhile, model-based approaches have difficulty in identifying a small, clustered set of anomalies.

We propose Information-theoretic Meta-clustering (ITMC), a formalization of model-based clustering principled by the theory of lossy data compression. ITMC identifies a `unique' cluster whose distribution diverges significantly from the entire dataset. Furthermore, ITMC employs a regularization term derived from the preference for high compression rate, which is critical to the precision of detection.

For empirical evaluation, we apply ITMC to two temporal anomaly detection tasks. Datasets are taken from generative processes involving heterogeneous and inconsistent dynamics. A comparison to baseline methods shows that the proposed algorithm detects segments from irregular states with significantly high precision and recall.