Walk-Forward Validation for Time Series foundational
Why model evaluation must preserve temporal order, and how expanding or rolling splits approximate live deployment.
Why model evaluation must preserve temporal order, and how expanding or rolling splits approximate live deployment.
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References
Advances in Financial Machine Learning
Marcos Lopez de Prado
(2018)
— John Wiley & Sons
Deep Learning for Financial Time Series: A Large-Scale Benchmark of Risk-Adjusted Performance
Adir Saly-Kaufmann, Kieran Wood, Jan Peter-Calliess, Stefan Zohren
(2026)
Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations
Daniel S. Brown, Wonjoon Goo, Prabhat Nagarajan, Scott Niekum
(2019)
— arXiv.org
Coherent Measures of Risk
Philippe Artzner, Freddy Delbaen, Jean-Marc Eber, David Heath
(1999)
— Mathematical Finance
Statistical Overfitting and Backtest Performance
David H. Bailey, Stephanie Ger, Marcos Lopez de Prado, Alexander Sim, Kesheng Wu
(2014)
Enhanced Backtesting for Practitioners
Jacques Joubert, Dragan Sestovic, Illya Barziy, Walter Distaso, Marcos López de Prado
(2024)
— The Journal of Portfolio Management