Chapter 9: Model-Based Feature Extraction

Regime Models for Feature Engineering: HMMs, Markov Switching, and Distributional Clustering advanced

Chapter 9 already makes the operational point about filtered regime probabilities. This primer narrows to the more technical layer: the filtering recursion, state uncertainty, and the identification problems that make regime models easy to misuse.

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Chapter

9 Model-Based Feature Extraction

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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 Path Signatures and Log-Signatures for Financial Sequences 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