#numerical-experiments

#numerical-experiments

The (K)-SIF method improves anomaly detection by effectively utilizing signature measures, demonstrating superior robustness and performance against traditional methods.

Adjusting the signature depth parameter is vital for optimizing the algorithm's performance in various scenarios.

The Signature Isolation Forest method significantly improves anomaly detection in complex data sets compared to traditional methods.

Functional Isolation Forests leverage statistical randomness to identify anomalies in data.

Signature Isolation Forest aims to improve anomaly detection by overcoming limitations of the Functional Isolation Forest, using the signature method for enhanced accuracy.