Anomaly detection of automatic online extreme studentized deviate tests for pattern-changing streaming data

The package osESD in Python and R implements online sequential extreme studentized deviate tests , or osESD in short, a statistical testing method for anomaly detection in online streaming datasets with varying patterns. Its foundation lies in the general extreme studentized deviate test, or GESD test. The GESD test, a simple and effective anomaly detection test, lacks online learning and shows poor performance for pattern-varying data. Our proposed osESD algorithm uses sequential ESD, a variation of GESD, which implements online learning with change-rate trend vectors and residual trend vectors to deal with the problem of pattern-varying streaming data. We also automated parameter tuning in the package with multiple options the user can select for immediate deployment and use. We show that the model learning of our implementation is fast and the performance is excellent compared with numerous models with both various synthetic datasets and real datasets. Codes can be found in the PyPI [ 11 ].