Chapter 9: Model-Based Feature Extraction

Fractional Differencing: Keeping Memory Without Keeping the Unit Root advanced

How to turn differencing from a blunt preprocessing step into a tunable filter that trades stationarity against memory retention.

How to turn differencing from a blunt preprocessing step into a tunable filter that trades stationarity against memory retention.

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References

Advances in Financial Machine Learning

Marcos Lopez de Prado (2018) — John Wiley & Sons

Generalized autoregressive conditional heteroskedasticity

Tim Bollerslev (1986) — Journal of Econometrics

Chapter

9 Model-Based Feature Extraction

More Topics

Autoregressive, Moving-Average, and ARIMA Foundations for Feature Engineering Bayesian Inference and MCMC for Time Series Path Signatures and Log-Signatures for Financial Sequences 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