knn_forecasting | R Documentation |

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.

```
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")
)
```

`timeS` |
A numeric vector or time series of class |

`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 |

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 |

```
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
```

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