tsfknn (version 0.6.0)

knn_forecasting: Time series forecasting using KNN regression

Description

It applies KNN regression to forecast the future values of a time series. The lags used as autoregressive variables are set with the lags parameter. If the user does not set the number of nearest neighbors or the lags, these values are selected automatically.

Usage

knn_forecasting(
  timeS,
  h,
  lags = NULL,
  k = c(3, 5, 7),
  msas = c("recursive", "MIMO"),
  cf = c("mean", "median", "weighted"),
  transform = c("additive", "multiplicative", "none")
)

Value

An object of class "knnForecast". The function summary can be used to obtain or print a summary of the results.

An object of class \code{"knnForecast"} is a list containing at least
the following components:

call

the matched call.

msas

the Multi-Step Ahead Strategy.

prediction

a time series with the forecast.

model

an object of class "knnModel" with the KNN model

Arguments

timeS

A numeric vector or time series of class ts.

h

A positive integer. Number of values to forecast.

lags

An integer vector in increasing order expressing the lags used as autoregressive variables.

k

A positive integer. The k parameter in KNN regression. A vector of k values can also be used. In that case, the forecast is the average of the forecasts produced by the different models with the different k parameters.

msas

A string indicating the Multiple-Step Ahead Strategy used when more than one value is predicted. It can be "recursive" or "MIMO" (the default).

cf

A string. It indicates the combination function used to aggregate the targets associated with the nearest neighbors. It can be "median", "weighted" or "mean" (the default).

transform

A character value indicating whether the training samples are transformed. If the time series has a trend it is recommended. By default is "multiplicative" (multiplicative transformation). It is also possible a multiplicative transformation or no transformation.

Examples

pred <- knn_forecasting(USAccDeaths, h = 12, lags = 1:12, k = 2)
pred$prediction # To see a time series with the forecasts
plot(pred) # To see a plot with the forecast