Chapter 9: Model-Based Feature Extraction

Wavelets for Multi-Scale Diagnostics and Causal Feature Design advanced

Wavelets are most useful in ML4T when they reveal which horizon matters and then disappear behind a trailing, auditable feature.

Wavelets are most useful in ML4T when they reveal which horizon matters and then disappear behind a trailing, auditable feature.

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References

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

Kieran Wood, Sven Giegerich, Stephen Roberts, Stefan Zohren (2023)

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 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