https://github.com/cran/tsfknn
Tip revision: 71a26cefc92d5de046b866de5dde4f749c6dcf72 authored by Francisco Martinez on 20 December 2023, 16:20:02 UTC
version 0.6.0
version 0.6.0
Tip revision: 71a26ce
NEWS.md
# tsfknn
* Bug fixed computing weights when neighbors are weighted by distance
* When lags are selected automatically it is not allowed only one lag
and the additive or multiplicative transformation
* It is not allowed only one autoregressive lag and the additive or
multiplicative transformation
* More information in the vignette about transformations
# tsfknn 0.5.2
* bug fixed in rolling_origin
* modifying tsfknn-package.R to comply with CRAN
# tsfknn 0.5.1
* autoplot.knnForecast has been modified to comply with CRAN
# tsfknn 0.5.0
* The default Multi-step ahead strategy is recursive
* An optional transformation to the training samples has been added. It improves forecast accuracy for time series with a trend
* When several k are used, only those k that are equal or lower than
the number of training samples are admitted
# tsfknn 0.4.0
* Using Rcpp for faster computation of nearest neighbors
# tsfknn 0.3.1
* Fix calculation of rolling origin prediction with recursive strategy
# tsfknn 0.3.0
* Now it is possible to assess the model using rolling origin evaluation
* A predict method has been added to generate new forecasts based on a
previously built model
# tsfknn 0.2.0
* summary and print.summary methods are added for "knnForecast" objects
* String parameters are processed with match.arg
* Fix calculation of how many KNN examples has the model in knn_forecasting
* Weighted combination of the targets of nearest neighbors is implemented
* A function that computes the number of training instances that would have
a model has been added