NEWS.md

In mtrupiano1/knnwtsim: K Nearest Neighbor Forecasting with a Tailored Similarity Metric

knnwtsim 1.0.0

Major Changes

• The function knn.forecast.boot.intervals() has been added to provide the capability to generate interval forecasts and simulations with the package.

• The default value of max.k in knn.forecast.randomsearch.tuning() has been changed from NA to NULL, to bring it in line with the approach I took to optional arguments elsewhere in the package. However, passing max.k = NA will still work, and behave in the same manner as max.k = NULL.

• Changing the behavior of max.k in knn.forecast.randomsearch.tuning() when the current or previous default argument is passed to be more dynamic based on the length of the input series. Now max.k will be set to min(floor((length(y.in)) * .4), length(y.in) - val.holdout.len - test.h) if NULL or NA is passed. This change was made because the more arbitrary behavior in knnwtsim 0.1.0 where max.k would be set to min(floor((length(y.in)) * .4), 50) if NA was passed could lead to errors.

• Error handling to throw errors or warnings for conflicting arguments, arguments outside reasonable ranges, and argument types differing from those listed in the help files has been added to all user facing functions in the package: StMatrixCalc(), SpMatrixCalc(), SxMatrixCalc(), SwMatrixCalc(), knn.forecast(), knn.forecast.randomsearch.tuning(), and knn.forecast.boot.intervals().

Minor Changes

• A min.k argument has been added to knn.forecast.randomsearch.tuning(), which can be used set a floor for the minimum number of nearest neighbors to be proposed in any parameter set to be tested in tuning. By default min.k = 1, in line with previous behavior of the function.

• Added references to arXiv:2112.06266 to DESCRIPTION and help files as needed to provide more information on methodology.

mtrupiano1/knnwtsim documentation built on March 20, 2022, 5:22 a.m.