ATAforecasting: Automatic Time Series Analysis and Forecasting Using the Ata Method

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.

Package details

AuthorAli 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>)
MaintainerAli Sabri Taylan <alisabritaylan@gmail.com>
LicenseGPL-3
Version0.0.52
URL https://github.com/alsabtay/ATAforecasting https://atamethod.wordpress.com/
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("ATAforecasting")

Try the ATAforecasting package in your browser

Any scripts or data that you put into this service are public.

ATAforecasting documentation built on March 8, 2021, 9:07 a.m.