Chapter 9: Model-Based Feature Extraction

Stationarity Tests: ADF, KPSS, and Rolling Stability Signals intermediate

These tests are useful in trading because they summarize how stable a series looks right now, not because they can certify once and for all that a process is stationary.

Register to Read

Create Free Account

Already have an account? Sign in

References

Advances in Financial Machine Learning

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

Selecting appropriate methodological framework for time series data analysis

Min B. Shrestha, Guna R. Bhatta — The Journal of Finance and Data Science

Co-Integration and Error Correction: Representation, Estimation, and Testing

Robert F. Engle, C. W. J. Granger (1987) — Econometrica

Chapter

9 Model-Based Feature Extraction

More Topics

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 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