Anomaly Detection and Change Point Detection - Reproduced
Ano malies are patterns in the data that do not conform to a well-defined notion of normal behavior. Techniques used to detection anomalies typically require training before using on new data. Here we will reproduce the results from Oana Niculaescu 's article in XRDS , Applying Data Science for Anomaly and Change Point Detection . This article was generated using a Jupyter Notebook. The notebook is available here . Detecting Changes The CUSUM algorithm is used to test for anomalies. This requires two parameters: threshold and drift . But, how do you choose values for these parameters? Gustafsson (2000) provides this recipe: Start with a very large threshold . Choose drift to one half of the expected change, or adjust drift such that g = 0 more than 50% of the time. Then set the threshold so the required number of false alarms (this can be done automatically) or delay for detection is obtained...