Optimal Out-of-Sample Forecast Evaluation and Testing under Stationarity R package ACV version 1.0.2 Package 'ACV' (short for Affine Cross-Validation) offers an improved time-series cross-validation loss estimator which utilizes both in-sample and out-of-sample forecasting performance via a carefully constructed affine weighting scheme. Under the assumption of stationarity, the estimator is the best linear unbiased estimator of the out-of-sample loss. Besides that, the package also offers improved versions of Diebold-Mariano and Ibragimov-Muller tests of equal predictive ability which deliver more power relative to their conventional counterparts. For more information, see the accompanying article Stanek (2021) <doi:10.2139/ssrn.3996166>. Software Imports: forecast, Matrix, methods, stats Suggests: testthat Filip Stanek <stanek.fi@gmail.com> Comprehensive R Archive Network (CRAN) Filip Stanek [aut, cre] GPL (>= 3) 2022-04-05 application/tgz https://CRAN.R-project.org/package=ACV doi:10.32614/CRAN.package.ACV