ATA.Forecast {ATAforecasting}

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

#'Yapar G, Yavuz I, Selamlar HT (2017). “Why and How Does Exponential Smoothing Fail? An In Depth Comparison of ATA-Simple and Simple Exponential Smoothing.” Turkish Journal of Forecasting, 1(1), 30–39.

#'Yapar G, Capar S, Selamlar HT, Yavuz I (2018). “Modified Holt's Linear Trend Method.” Hacettepe University Journal of Mathematics and Statistics, 47(5), 1394–1403.

#'Yapar G (2018). “Modified simple exponential smoothing.” Hacettepe University Journal of Mathematics and Statistics, 47(3), 741–754.

#'Yapar G, Selamlar HT, Capar S, Yavuz I (2019). “ATA method.” Hacettepe Journal of Mathematics and Statistics, 48(6), 1838-1844.

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)