The Ata method (Yapar et al. (2019) <doi:10.15672/hujms.461032>), an alternative to exponential smoothing (described in Yapar (2016) <doi:10.15672/HJMS.201614320580>, Yapar et al. (2017) <doi:10.15672/HJMS.2017.493>), 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’s PhD dissertation.
|Author||Ali Sabri Taylan [aut, cre, cph] (<https://orcid.org/0000-0001-9514-934X>), Hanife Taylan Selamlar [aut, cph] (<https://orcid.org/0000-0002-4091-884X>), Guckan Yapar [aut, ths, cph] (<https://orcid.org/0000-0002-0971-6676>)|
|Maintainer||Ali Sabri Taylan <firstname.lastname@example.org>|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.