ARml {caretForecast}

Autoregressive forecasting using various Machine Learning models.

Description

Autoregressive forecasting using various Machine Learning models.

Usage

ARml(
  y,
  max_lag = 5,
  xreg = NULL,
  caret_method = "cubist",
  metric = "RMSE",
  pre_process = NULL,
  cv = TRUE,
  cv_horizon = 4,
  initial_window = NULL,
  fixed_window = FALSE,
  verbose = TRUE,
  seasonal = TRUE,
  K = frequency(y)/2,
  tune_grid = NULL,
  lambda = NULL,
  BoxCox_method = c("guerrero", "loglik"),
  BoxCox_lower = -1,
  BoxCox_upper = 2,
  BoxCox_biasadj = FALSE,
  BoxCox_fvar = NULL,
  allow_parallel = FALSE,
  ...
)

Arguments

y

A univariate time series object.

max_lag

Maximum value of lag.

xreg

Optional. 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.).

caret_method

A string specifying which classification or regression model to use. Possible values are found using names(getModelInfo()). A list of functions can also be passed for a custom model function. See http://topepo.github.io/caret/ for details.

metric

A string that specifies what summary metric will be used to select the optimal model. See ?caret::train.

pre_process

A string vector that defines a pre-processing of the predictor data. Current possibilities are "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica" and "spatialSign". The default is no pre-processing. See preProcess and trainControl on the procedures and how to adjust them. Pre-processing code is only designed to work when x is a simple matrix or data frame.

cv

Logical, if cv = TRUE model selection will be done via cross-validation. If cv = FALSE user need to provide a specific model via tune_grid argument.

cv_horizon

The number of consecutive values in test set sample.

initial_window

The initial number of consecutive values in each training set sample.

fixed_window

Logical, if FALSE, all training samples start at 1.

verbose

A logical for printing a training log.

seasonal

Boolean. If seasonal = TRUE the fourier terms will be used for modeling seasonality.

K

Maximum order(s) of Fourier terms

tune_grid

A data frame with possible tuning values. The columns are named the same as the tuning parameters. Use getModelInfo to get a list of tuning parameters for each model or see http://topepo.github.io/caret/available-models.html. (NOTE: If given, this argument must be named.)

lambda

BoxCox transformation parameter. If lambda = NULL If lambda = "auto", then the transformation parameter lambda is chosen using BoxCox.lambda.

BoxCox_method

BoxCox.lambda argument. Choose method to be used in calculating lambda.

BoxCox_lower

BoxCox.lambda argument. Lower limit for possible lambda values.

BoxCox_upper

BoxCox.lambda argument. Upper limit for possible lambda values.

BoxCox_biasadj

InvBoxCox argument. Use adjusted back-transformed mean for Box-Cox transformations. If transformed data is used to produce forecasts and fitted values, a regular back transformation will result in median forecasts. If biasadj is TRUE, an adjustment will be made to produce mean forecasts and fitted values.

BoxCox_fvar

InvBoxCox argument. Optional parameter required if biasadj=TRUE. Can either be the forecast variance, or a list containing the interval level, and the corresponding upper and lower intervals.

allow_parallel

If a parallel backend is loaded and available, should the function use it?

...

Ignored.

Value

A list class of forecast containing the following elemets

• x : The input time series

• method : The name of the forecasting method as a character string

• mean : Point forecasts as a time series

• lower : Lower limits for prediction intervals

• upper : Upper limits for prediction intervals

• level : The confidence values associated with the prediction intervals

• model : A list containing information about the fitted model

• newx : A matrix containing regressors

Author(s)

Resul Akay

Examples

library(caretForecast)

train_data <- window(AirPassengers, end = c(1959, 12))

test <- window(AirPassengers, start = c(1960, 1))

ARml(train_data, caret_method = "lm", max_lag = 12) -> fit

forecast(fit, h = length(test)) -> fc

autoplot(fc) + autolayer(test)

accuracy(fc, test)