## 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