The R Journal: article published in 2019, volume 11:2

Time Series Forecasting with KNN in R: the tsfknn Package
Francisco Martínez, María P. Frías, Francisco Charte and Antonio J. Rivera , The R Journal (2019) 11:2, pages 229-242.

Abstract In this paper the tsfknn package for time series forecasting using k-nearest neighbor regres sion is described. This package allows users to specify a KNN model and to generate its forecasts. The user can choose among different multi-step ahead strategies and among different functions to aggregate the targets of the nearest neighbors. It is also possible to assess the forecast accuracy of the KNN model.

Received: 2018-05-29; online 2019-07-30, supplementary material, (718 B)
CRAN packages: forecast, caret, nnfor, tsfknn, forecastHybrid, GMDH, NTS, tsDyn, nnet, neuralnet
CRAN Task Views implied by cited CRAN packages: TimeSeries, Econometrics, Finance, MachineLearning, Environmetrics, HighPerformanceComputing, MissingData, Multivariate, SocialSciences

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-004,
  author = {Francisco Martínez and María P. Frías and Francisco Charte
          and Antonio J. Rivera},
  title = {{Time Series Forecasting with KNN in R: the tsfknn Package}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-004},
  url = {https://doi.org/10.32614/RJ-2019-004},
  pages = {229--242},
  volume = {11},
  number = {2}
}