forecast (version 9.0.0)

modelAR: Time Series Forecasts with a user-defined model

Description

Experimental function to forecast univariate time series with a user-defined model

Usage

modelAR(
  y,
  p,
  P = 1,
  FUN,
  predict.FUN,
  xreg = NULL,
  lambda = NULL,
  model = NULL,
  subset = NULL,
  scale.inputs = FALSE,
  x = y,
  ...
)

Value

Returns an object of class modelAR.

The function summary is used to obtain and print a summary of the results.

The generic accessor functions fitted.values and residuals

extract useful features of the value returned by modelAR.

model

A list containing information about the fitted model

method

The name of the forecasting method as a character string

x

The original time series.

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

Arguments

y

a numeric vector or univariate time series of class ts

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.

FUN

Function used for model fitting. Must accept argument x and y for the predictors and response, respectively (formula object not currently supported).

predict.FUN

Prediction function used to apply FUN to new data. Must accept an object of class FUN as its first argument, and a data frame or matrix of new data for its second argument. Additionally, it should return fitted values when new data is omitted.

xreg

Optionally, a numerical vector or matrix of external regressors, which must have the same number of rows as y. It should not be a data frame.

lambda

Box-Cox transformation parameter. If lambda = "auto", then a transformation is automatically selected using BoxCox.lambda. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.

model

Output from a previous call to nnetar. If model is passed, this same model is fitted to y without re-estimating any parameters.

subset

Optional vector specifying a subset of observations to be used in the fit. Can be an integer index vector or a logical vector the same length as y. All observations are used by default.

scale.inputs

If TRUE, inputs are scaled by subtracting the column means and dividing by their respective standard deviations. If lambda is not NULL, scaling is applied after Box-Cox transformation.

x

Deprecated. Included for backwards compatibility.

...

Other arguments passed to FUN for modelAR.

Author

Rob J Hyndman and Gabriel Caceres

Details

This is an experimental function and only recommended for advanced users. The selected model is fitted with lagged values of y as inputs. The inputs are for lags 1 to p, and lags mtomPwherem = frequency(y). If xregis provided, its columns are also used as inputs. If there are missing values inyorxreg`, the corresponding rows (and any others which depend on them as lags) are omitted from the fit. The model is trained for one-step forecasting. Multi-step forecasts are computed recursively.

Examples

## Set up functions
my_lm <- function(x, y) {
 structure(lsfit(x,y), class = "lsfit")
}
predict.lsfit <- function(object, newdata = NULL) {
  n <- length(object$qr$qt)
  if(is.null(newdata)) {
    z <- numeric(n)
    z[seq_len(object$qr$rank)] <- object$qr$qt[seq_len(object$qr$rank)]
    as.numeric(qr.qy(object$qr, z))
  } else {
    sum(object$coefficients * c(1, newdata))
  }
}
# Fit an AR(2) model
fit <- modelAR(
  y = lynx,
  p = 2,
  FUN = my_lm,
  predict.FUN = predict.lsfit,
  lambda = 0.5,
  scale.inputs = TRUE
)
forecast(fit, h = 20) |> autoplot()