Chapter 9: Model-Based Feature Extraction

Autoregressive, Moving-Average, and ARIMA Foundations for Feature Engineering intermediate

ARIMA is rarely the star predictor in liquid markets, but it is still one of the cleanest ways to separate level, persistence, shock, and forecast uncertainty before downstream models take over.

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References

Conditional Heteroskedasticity in Asset Returns: A New Approach

Daniel B. Nelson (1991) — Econometrica

A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle

James D. Hamilton (1989) — Econometrica

Empirical properties of asset returns: stylized facts and statistical issues

R. Cont (2001) — Quantitative Finance

Chapter

9 Model-Based Feature Extraction

More Topics

Bayesian Inference and MCMC for Time Series Fractional Differencing: Keeping Memory Without Keeping the Unit Root 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