ATA.Forecast: Forecasting Method for The ATAforecasting

In alsabtay/ATAforecasting: Automatic Time Series Analysis and Forecasting using the Ata Method

Forecasting Method for The ATAforecasting

Description

ATA.Forecast is a generic function for forecasting of the ATA Method.

Usage

ATA.Forecast(
  object,
  h = NULL,
  out.sample = NULL,
  ci.level = 95,
  negative.forecast = TRUE,
  onestep = FALSE,
  print.out = TRUE
)

Arguments

object	An ata object is required for forecast.
h	Number of periods for forecasting.
out.sample	A numeric vector or time series of class ts or msts for out-sample.
ci.level	Confidence Interval levels for forecasting. Default value is 95.
negative.forecast	Negative values are allowed for forecasting. Default value is TRUE. If FALSE, all negative values for forecasting are set to 0.
onestep	Default is FALSE. if TRUE, the dynamic forecast strategy uses a one-step model multiple times (h forecast horizon) where the prediction for the prior time step is used as an input for making a prediction on the following time step.
print.out	Default is TRUE. If FALSE, forecast summary of ATA Method is not shown.

Value

An object of class ata and forecast values.

Author(s)

Ali Sabri Taylan and Hanife Taylan Selamlar

References

#'\insertRefyapar2017msesATAforecasting

#'\insertRefyapar2018mhesATAforecasting

#'\insertRefyapar2018msesATAforecasting

#'\insertRefyapar2019ataATAforecasting

See Also

forecast, stlplus, stR, stl, decompose, tbats, seasadj.

Examples

trainATA <-  head(touristTR, 84)
ata_fit <- ATA(trainATA, parPHI = 1, seasonal.test = TRUE, seasonal.model = "decomp")
ata_fc <- ATA.Forecast(ata_fit, h=12)

alsabtay/ATAforecasting documentation built on July 3, 2023, 3:42 p.m.