How K-SIF and SIF Revolutionize Anomaly Detection in Complex Datasets

K-SIF and SIF offer significant advancements in anomaly detection by handling non-linear data and eliminating the need for predefined dictionaries. They consistently outperform FIF in real-world datasets, with SIF providing a simple yet highly effective solution for functional anomaly detection.

Table of Links

Abstract and 1. Introduction

2. Background & Preliminaries

2.1. Functional Isolation Forest

2.2. The Signature Method

3. Signature Isolation Forest Method

4. Numerical Experiments

4.1. Parameters Sensitivity Analysis

4.2. Advantages of (K-)SIF over FIF

4.3. Real-data Anomaly Detection Benchmark

5. Discussion & Conclusion, Impact Statements, and References

Appendix

A. Additional Information About the Signature

B. K-SIF and SIF Algorithms

C. Additional Numerical Experiments

5. Discussion & Conclusion

This work presents two novel anomaly detection algorithms, K-SIF and SIF, rooted in the isolation forest structure and the signature approach from ...