nnetar {forecast} | R Documentation |
Feed-forward neural networks with a single hidden layer and lagged inputs for forecasting univariate time series.
nnetar(x, p, P=1, size, repeats=20, lambda=NULL) ## S3 method for class 'nnetar' forecast(object, h=ifelse(object$m > 1, 2 * object$m, 10), lambda=object$lambda, ...)
x |
a numeric vector or time series |
p |
Embedding dimension for non-seasonal time series. Number of non-seasonal lags used as inputs. For non-seasonal time series, the default is the optimal number of lags (according to the AIC) for a linear AR(p) model. For seasonal time series, the same method is used but applied to seasonally adjusted data (from an stl decomposition). |
P |
Number of seasonal lags used as inputs. |
size |
Number of nodes in the hidden layer. Default is half of the number of input nodes plus 1. |
repeats |
Number of networks to fit with different random starting weights. These are then averaged when producing forecasts. |
lambda |
Box-Cox transformation parameter. |
object |
An object of class |
h |
Number of periods for forecasting. |
... |
Other arguments. |
A feed-forward neural network is fitted with lagged values of x
as inputs and a single hidden layer with size
nodes. The inputs are for lags 1 to p
, and lags m
to mP
where m=frequency(x)
. A total of repeats
networks are fitted, each with random starting weights. These are then averaged when computing forecasts. The network is trained for one-step forecasting. Multi-step forecasts are computed recursively. The fitted model is called an NNAR(p,P) model and is analogous to an ARIMA(p,0,0)(P,0,0) model but with nonlinear functions.
nnetar
returns an object of class "nnetar
". forecast.nnetar
returns an object of class "forecast
".
The function summary
is used to obtain and print a summary of the
results, while the function plot
produces a plot of the forecasts.
The generic accessor functions fitted.values
and residuals
extract useful features of the value returned by nnetar
.
An object of class "forecast"
is a list containing at least the following elements:
model |
A list containing information about the fitted model |
method |
The name of the forecasting method as a character string |
mean |
Point forecasts as a time series |
x |
The original time series (either |
residuals |
Residuals from the fitted model. That is x minus fitted values. |
fitted |
Fitted values (one-step forecasts) |
... |
Other arguments |
Rob J Hyndman
fit <- nnetar(lynx) fcast <- forecast(fit) plot(fcast)