ACV
Optimal Out-of-Sample Forecast Evaluation and Testing under Stationarity
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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.2 |
rolling linux/jammy R-4.5 | ACV_1.0.2.tar.gz |
48.7 KiB |
1.0.2 |
rolling linux/noble R-4.5 | ACV_1.0.2.tar.gz |
48.7 KiB |
1.0.2 |
rolling source/ R- | ACV_1.0.2.tar.gz |
10.0 KiB |
1.0.2 |
latest linux/jammy R-4.5 | ACV_1.0.2.tar.gz |
48.7 KiB |
1.0.2 |
latest linux/noble R-4.5 | ACV_1.0.2.tar.gz |
48.7 KiB |
1.0.2 |
latest source/ R- | ACV_1.0.2.tar.gz |
10.0 KiB |
1.0.2 |
2026-04-26 source/ R- | ACV_1.0.2.tar.gz |
10.0 KiB |
1.0.2 |
2026-04-23 source/ R- | ACV_1.0.2.tar.gz |
10.0 KiB |
1.0.2 |
2026-04-09 windows/windows R-4.5 | ACV_1.0.2.zip |
51.3 KiB |
1.0.2 |
2025-04-20 source/ R- | ACV_1.0.2.tar.gz |
10.0 KiB |