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.

Explore how split windows in Signature Isolation Forests enhance anomaly detection, balancing accuracy and efficiency for isolated and persistent anomalies.

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.