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, xreg=NULL, lambda=NULL, model=NULL, scale.inputs=TRUE, ...) ## S3 method for class 'nnetar' forecast(object, h=ifelse(object$m > 1, 2 * object$m, 10), xreg=NULL, 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 (including external regressors, if given) plus 1. |
repeats |
Number of networks to fit with different random starting weights. These are then averaged when producing forecasts. |
xreg |
Optionally, a vector or matrix of external regressors, which must have the same number of rows as x. Must be numeric. |
lambda |
Box-Cox transformation parameter. |
model |
Output from a previous call to |
scale.inputs |
If TRUE, inputs are scaled by subtracting the column means and dividing by their respective standard deviations. |
object |
An object of class |
h |
Number of periods for forecasting. |
... |
Other arguments passed to |
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)
. If there are missing values in x
or xreg
), the corresponding rows (and any others which depend on them as lags) are omitted from the fit. 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 |
xreg |
The external regressors used in fitting (if given). |
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) # Arguments can be passed to nnet() fit <- nnetar(lynx, decay=0.5, maxit=150) plot(forecast(fit)) lines(lynx) # Fit model to first 100 years of lynx data fit <- nnetar(window(lynx,end=1920), decay=0.5, maxit=150) plot(forecast(fit,h=14)) lines(lynx) # Apply fitted model to later data, including all optional arguments fit2 <- nnetar(window(lynx,start=1921), model=fit)