Inductive conformal anomaly detection for sequential detection of anomalous sub-trajectories

• Published in: Annals of mathematics and artificial intelligence Vol. 74; no. 1-2; pp. 67 - 94
• Main Authors: Laxhammar, Rikard, Falkman, Göran
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2015; Springer Nature B.V
• Subjects: Algorithms; Anomalies; Anomaly detection; Artificial Intelligence; Complex Systems; Computer Science; Conformal prediction; Data analysis; Design parameters; Empirical analysis; Local outlier factor; Machine learning; Maritime surveillance; Mathematics; Outliers (statistics); Skövde Artificial Intelligence Lab (SAIL); Technology; Teknik; Trajectory data; 68T10; 68T05; Maritime surveillance; Trajectory data; Local outlier factor; Anomaly detection; Conformal prediction
• DOI: 10.1007/s10472-013-9381-7
• ISSN: 1012-2443; 1573-7470

Detection of anomalous trajectories is an important problem for which many algorithms based on learning of normal trajectory patterns have been proposed. Yet, these algorithms are typically designed for offline anomaly detection in databases and are insensitive to local sub-trajectory anomalies. Generally, previous anomaly detection algorithms often require tuning of many parameters, including ad-hoc anomaly thresholds, which may result in overfitting and high alarm rates. The main contributions of this paper are two-fold: The first is the proposal and analysis of the Inductive Conformal Anomaly Detector (ICAD), which is a general and parameter-light anomaly detection algorithm that has well-calibrated alarm rate. ICAD is a generalisation of the previously proposed Conformal Anomaly Detector (CAD) based on the concept of Inductive Conformal Predictors . The main advantage of ICAD compared to CAD is the improved computational efficiency. The only design parameter of ICAD is the Non-Conformity Measure (NCM). The second contribution of this paper concerns the proposal and investigation of the Sub-Sequence Local Outlier (SSLO) NCM, which is designed for sequential detection of anomalous sub-trajectories in the framework of ICAD. SSLO-NCM is based on Local Outlier Factor (LOF) and is therefore sensitive to local sub-trajectory anomalies. The results from the empirical investigations on an unlabelled set of vessel trajectories illustrate the most anomalous trajectories detected for different parameter values of SSLO-NCM, and confirm that the empirical alarm rate is indeed well-calibrated.