Editing Cross-validation (statistics) (section)

==Cross validation for time-series models==
{{See also|Blocking (statistics)}}
Due to correlations, cross-validation with random splits might be problematic for [[time-series]] models (if we are more interested in evaluating extrapolation, rather than interpolation).<ref>Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure https://nsojournals.onlinelibrary.wiley.com/doi/10.1111/ecog.02881</ref> A more appropriate approach might be to use rolling cross-validation.<ref>{{cite journal |last1=Bergmeir |first1=Christoph |last2=Benítez |first2=José M. |title=On the use of cross-validation for time series predictor evaluation |journal=Information Sciences |date=May 2012 |volume=191 |pages=192–213 |doi=10.1016/j.ins.2011.12.028 }}</ref>

However, if performance is described by a single [[summary statistic]], it is possible that the approach described by Politis and Romano as a [[stationary bootstrap]]<ref>{{cite journal |last1=Politis |first1=Dimitris N. |last2=Romano |first2=Joseph P. |title=The Stationary Bootstrap |journal=Journal of the American Statistical Association |date=December 1994 |volume=89 |issue=428 |pages=1303–1313 |doi=10.1080/01621459.1994.10476870 |hdl=10983/25607 |hdl-access=free }}</ref> will work. The statistic of the bootstrap needs to accept an interval of the time series and return the summary statistic on it. The call to the stationary bootstrap needs to specify an appropriate mean interval length.