Chapter 9: Model-Based Feature Extraction

State-Space Models and Kalman Filtering for Feature Engineering advanced

How linear Gaussian state-space models turn noisy time series into point-in-time features such as level, trend, innovation, and uncertainty.

How linear Gaussian state-space models turn noisy time series into point-in-time features such as level, trend, innovation, and uncertainty.

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References

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

James D. Hamilton (1989) — Econometrica

Pairs trading

Robert J. Elliott, John Van Der Hoek *, William P. Malcolm (2005) — Quantitative Finance

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 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