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ATAforecasting  

Automatic Time Series Analysis and Forecasting using the Ata Method
View on CRAN: Click here


Download and install ATAforecasting package within the R console
Install from CRAN:
install.packages("ATAforecasting")

Install from Github:
library("remotes")
install_github("cran/ATAforecasting")

Install by package version:
library("remotes")
install_version("ATAforecasting", "0.0.61")



Attach the package and use:
library("ATAforecasting")
Maintained by
Ali Sabri Taylan
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-03-08
Latest Update: 2025-09-03
Description:
The Ata method (Yapar et al. (2019) ), an alternative to exponential smoothing (described in Yapar (2016) , Yapar et al. (2017) ), is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing forecasting methods. Forecasting performance of the Ata method is superior to existing methods both in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or seasonal time series which can be decomposed into four components (remainder, level, trend and seasonal). This methodology performed well on the M3 and M4-competition data. This package was written based on Ali Sabri Taylan
How to cite:
Ali Sabri Taylan (2021). ATAforecasting: Automatic Time Series Analysis and Forecasting using the Ata Method. R package version 0.0.61, https://cran.r-project.org/web/packages/ATAforecasting. Accessed 16 Jul. 2026.
Previous versions and publish date:
0.0.52 (2021-03-08 08:40), 0.0.53 (2021-10-11 09:20), 0.0.54 (2021-10-20 09:10), 0.0.55 (2021-10-22 16:30), 0.0.56 (2021-12-08 22:10), 0.0.57 (2022-04-22 22:40), 0.0.60 (2023-06-12 12:50), (2026-07-09 07:57)
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Complete documentation for ATAforecasting
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