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.

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References

A Test for Superior Predictive Ability

Peter Reinhard Hansen (2005) — Journal of Business & Economic Statistics

Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture

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

Deep Learning with Differential Privacy

Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang (2016)

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Hena Ghonia, Rishika Bhagwatkar, Arian Khorasani, Mohammad Javad Darvishi Bayazi, George Adamopoulos, Roland Riachi, Nadhir Hassen, Marin Biloš, Sahil Garg, Anderson Schneider, Nicolas Chapados, Alexandre Drouin, Valentina Zantedeschi, Yuriy Nevmyvaka, Irina Rish (2024)

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 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