  In machine learning, statistics, econometrics and statistical physics,
cross-validation (CV) is used asa standard approach in quantifying the
generalisation performance of a statistical model. A directapplication of CV in
time-series leads to the loss of serial correlations, a requirement of
preserving anynon-stationarity and the prediction of the past data using the
future data. In this work, we proposea meta-algorithm called reconstructive
cross validation (rCV ) that avoids all these issues. At first,k folds are
formed with non-overlapping randomly selected subsets of the original
time-series. Then,we generate k new partial time-series by removing data points
from a given fold: every new partialtime-series have missing points at random
from a different entire fold. A suitable imputation or asmoothing technique is
used to reconstruct k time-series. We call these reconstructions
secondarymodels. Thereafter, we build the primary k time-series models using
new time-series coming fromthe secondary models. The performance of the primary
models are evaluated simultaneously bycomputing the deviations from the
originally removed data points and out-of-sample (OSS) data.Full
cross-validation in time-series models can be practiced with rCV along with
generating learning curves.
