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