ATA.Forecast {ATAforecasting} | R Documentation |

## 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 |

`h` |
Number of periods for forecasting. |

`out.sample` |
A numeric vector or time series of class |

`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 ( |

`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)
```

*ATAforecasting*version 0.0.60 Index]