Anomaly detection using Isolation Forest - Contextual Anomalies

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Isolation forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies, instead of the most common techniques of profiling normal points

In statistics, an anomaly (a.k.a. outlier) is an observation or event that deviates so much from other events to arouse suspicion it was generated by a different mean

A data point is considered a global outlier if its value is far outside the entirety of the data set in which it is found

A data point is considered a contextual outlier if its value significantly deviates from the rest of the data points in the same context. Note that this means that same value may not be considered an outlier if it occurred in a different context

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