Glossary · Business Analytics
Isolation Forest
also: iForest · tree-based anomaly detection
Definition
Isolation Forest is a tree-based anomaly detection algorithm that scores observations by how easily they can be isolated via random recursive partitioning. Anomalies are isolated in few splits; normal points require many. The algorithm handles mixed feature types without density estimation or distance calculations.
Liu, Ting and Zhou (2008) introduced isolation forest as an alternative to density-based outlier detection. Trees are built by random feature and split selection; path length (number of splits to isolate a point) is the anomaly score. The method scales linearly in sample size, handles high-dimensional data, and does not require anomaly examples to train. In revenue anomaly detection it complements Prophet-style decomposition by flagging pattern-breaking observations the time-series model cannot explain.
Essays on this concept
- Business Analytics
Anomaly Detection in Revenue Data: Isolation Forests vs. Prophet-Based Decomposition
A 4% revenue drop on a Tuesday could be a payment processor outage, a pricing bug, or just normal variance. The difference between these explanations is millions of dollars — and your monitoring system can't tell them apart.
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