Discover Signature Isolation Forest (SIF), a groundbreaking approach to functional anomaly detection.

Explore the mechanics of Functional Isolation Forest (FIF), a cutting-edge anomaly detection method utilizing random splits in functional Hilbert space.

Discover SIF and K-SIF, advanced anomaly detection methods using rough path theory, offering improved accuracy and flexibility over traditional approaches.

Discover the power of path signatures, a sequence of iterated integrals that encapsulate the geometric and topological essence of paths.

Explore how the signature method, tensor products, and closed-form computations enhance anomaly detection, providing powerful tools for functional data analysis

Explore how K-SIF and SIF, based on signature theory, enhance anomaly detection in complex functional data.

(K-)SIF outperforms FIF in anomaly detection, showcasing superior performance with the Brownian dictionary and strong robustness across real-data datasets.

Explore additional experiments on K-SIF and SIF, covering signature depth, noise robustness, and anomaly detection.

Peel back the layers of K-SIF and SIF algorithms, revealing cutting-edge anomaly detection methods that surpass traditional approaches like FIF.