Nothing
#' @keywords internal package ts
#' @aliases nnfor-package
"_PACKAGE"
#' nnfor:Time Series Forecasting with Neural Networks
#'
#' The \pkg{nnfor} package provides automatic time series modelling with neural networks. It facilitates fully automatic, semi-manual or fully manual specification of networks, using multilayer perceptrons (\code{\link{mlp}}) and extreme learning machines (\code{\link{elm}}).
#'
#' @note You can find a tutorial how to use the package \href{https://kourentzes.com/forecasting/2019/01/16/tutorial-for-the-nnfor-r-package/}{here}.
#'
#' @references
#' \itemize{
#' \item For an introduction to neural networks see: Ord K., Fildes R., Kourentzes N. (2017) \href{https://kourentzes.com/forecasting/2017/10/16/new-forecasting-book-principles-of-business-forecasting-2e/}{Principles of Business Forecasting 2e}. \emph{Wessex Press Publishing Co.}, Chapter 10.
#' \item For ensemble combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) \href{https://kourentzes.com/forecasting/2014/04/19/neural-network-ensemble-operators-for-time-series-forecasting/}{Neural network ensemble operators for time series forecasting}. \emph{Expert Systems with Applications}, \bold{41}(\bold{9}), 4235-4244.
#' \item For variable selection see: Crone S.F., Kourentzes N. (2010) \href{https://kourentzes.com/forecasting/2010/04/19/feature-selection-for-time-series-prediction-a-combined-filter-and-wrapper-approach-for-neural-networks/}{Feature selection for time series prediction – A combined filter and wrapper approach for neural networks}. \emph{Neurocomputing}, \bold{73}(\bold{10}), 1923-1936.
#' }
#'
#' @docType package
#'
#' @author Nikolaos Kourentzes, \email{nikolaos@kourentzes.com}
#'
#' @name nnfor
#'
#' @importFrom grDevices rainbow
#' @importFrom graphics arrows axis lines plot points text hist
#' @importFrom stats alias as.formula coef cor.test deltat end frequency friedman.test lm median runif start time ts ts.plot density fft uniroot predict
#' @importFrom utils head tail
#' @importFrom forecast nsdiffs
#' @importFrom generics forecast
#' @importFrom tsutils cmav lagmatrix seasdummy mseastest
#' @importFrom methods is
#' @importFrom uroot ch.test
#'
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.