Fit and forecast for benchmarking purposes

fitforecast(
  y,
  h = NULL,
  pct_train = 0.9,
  pct_calibration = 0.5,
  method = c("thetaf", "arima", "ets", "te", "tbats", "tslm", "dynrmf", "ridge2f",
    "naive", "snaive"),
  level = 95,
  B = 1000L,
  seed = 17223L,
  graph = TRUE,
  conformalize = FALSE,
  type_calibration = c("splitconformal", "cv1", "loocv"),
  gap = 3L,
  agg = c("mean", "median"),
  vol = c("constant", "garch"),
  type_sim = c("kde", "surrogate", "bootstrap"),
  ...
)

Arguments

y: A univariate time series of class ts
h: Forecasting horizon (default is NULL, in that case, pct_train and pct_calibration are used)
pct_train: Percentage of data in the training set, when h is NULL
pct_calibration: Percentage of data in the calibration set for conformal prediction
method: For now "thetaf" (default), "arima", "ets", "tbats", "tslm", "dynrmf" (from ahead), "ridge2f" (from ahead), "naive", "snaive"
level: Confidence level for prediction intervals in %, default is 95
B: Number of bootstrap replications or number of simulations (yes, 'B' is unfortunate)
seed: Reproducibility seed
graph: Plot or not?
conformalize: Calibrate or not?
type_calibration: "splitconformal" (default conformal method), "cv1" (do not use), "loocv" (do not use)
gap: length of training set for loocv conformal (do not use)
agg: "mean" or "median" (aggregation method) for
vol: "constant" or "garch" (type of volatility modeling for calibrated residuals)
type_sim: "kde", "surrogate", "bootstrap" (type of simulation for calibrated residuals)
...: additional parameters

Value

an object of class 'forecast' with additional information

Examples


par(mfrow=c(2, 2))
obj1 <- ahead::fitforecast(AirPassengers)
obj2 <- ahead::fitforecast(AirPassengers, conformalize = TRUE)
plot(AirPassengers)
plot(obj1)
obj2$plot()
obj2$plot("simulations")