Purged cross-validation

{{Orphan|date=June 2025}}

{{Orphan|date=June 2025}}

'''Purged cross-validation''' is a variant of k-fold [[Cross-validation (statistics)|cross-validation]] designed to prevent look-ahead bias in time series and other structured data, developed in 2017 by [[Marcos López de Prado]] at [[Guggenheim Partners]] and [[Cornell University]].<ref name="Lopez2018" /> It is primarily used in financial [[machine learning]] to ensure the independence of training and testing samples when labels depend on future events. It provides an alternative to conventional cross-validation and walk-forward [[backtesting]] methods, which often yield overly optimistic performance estimates due to information leakage and overfitting.<ref name="JPM" /><ref name="JCF" />

'''Purged cross-validation''' is a variant of ''k''-fold [[Cross-validation (statistics)|cross-validation]] designed to prevent look-ahead bias in time series and other structured data, developed in 2017 by [[Marcos López de Prado]] at [[Guggenheim Partners]] and [[Cornell University]].<ref name="Lopez2018" /> It is primarily used in financial [[machine learning]] to ensure the independence of training and testing samples when labels depend on future events. It provides an alternative to conventional cross-validation and walk-forward [[backtesting]] methods, which often yield overly optimistic performance estimates due to information leakage and overfitting.<ref name="JPM" /><ref name="JCF" />

== Motivation ==

== Motivation ==