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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.