Chapter 9: Model-Based Feature Extraction

Path Signatures and Log-Signatures for Financial Sequences advanced

Two windows can end at the same price with similar volatility and still trace different paths; signatures are one way to encode that ordered shape.

Two windows can end at the same price with similar volatility and still trace different paths; signatures are one way to encode that ordered shape.

Register to Read

Sign up for a free account to access all 112 primer topics.

Create Free Account

Already have an account? Sign in

References

Generating Financial Markets With Signatures

Hans Buehler, Blanka Horvath, Terry Lyons, Imanol Perez Arribas, Ben Wood (2020)

A Primer on the Signature Method in Machine Learning

Ilya Chevyrev, Andrey Kormilitzin, Christian Bayer, Goncalo dos Reis, Blanka Horvath, Harald Oberhauser (2026) — Springer Nature Switzerland

Chapter

9 Model-Based Feature Extraction

More Topics

Autoregressive, Moving-Average, and ARIMA Foundations for Feature Engineering Bayesian Inference and MCMC for Time Series Fractional Differencing: Keeping Memory Without Keeping the Unit Root Regime Models for Feature Engineering: HMMs, Markov Switching, and Distributional Clustering State-Space Models and Kalman Filtering for Feature Engineering Stationarity Tests: ADF, KPSS, and Rolling Stability Signals Structural Break Diagnostics and Time-Since-Break Features Uncertainty as a Feature Volatility Models as Feature Extractors: GARCH, EGARCH, and HAR Wavelets for Multi-Scale Diagnostics and Causal Feature Design