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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>.
| Version: | 1.0.2 |
| Imports: | forecast, Matrix, methods, stats |
| Suggests: | testthat |
| Published: | 2022-04-05 |
| Author: | Filip Stanek [aut, cre] |
| Maintainer: | Filip Stanek <stanek.fi at gmail.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | ACV results |
| Reference manual: | ACV.pdf |
| Package source: | ACV_1.0.2.tar.gz |
| Windows binaries: | r-devel: ACV_1.0.2.zip, r-release: ACV_1.0.2.zip, r-oldrel: ACV_1.0.2.zip |
| macOS binaries: | r-release (arm64): ACV_1.0.2.tgz, r-oldrel (arm64): ACV_1.0.2.tgz, r-release (x86_64): ACV_1.0.2.tgz, r-oldrel (x86_64): ACV_1.0.2.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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