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 Taylanâs PhD dissertation.
Version: 0.0.60 Depends: R (≥ 4.1) Imports: graphics, forecast, Rcpp, Rdpack, seasonal, stats, stlplus, stR, timeSeries, TSA, tseries, utils, xts LinkingTo: Rcpp, RcppArmadillo Published: 2023-06-12 DOI: 10.32614/CRAN.package.ATAforecasting Author: Ali Sabri Taylan [aut, cre, cph], Hanife Taylan Selamlar [aut, cph], Guckan Yapar [aut, ths, cph] Maintainer: Ali Sabri Taylan <alisabritaylan at gmail.com> BugReports: https://github.com/alsabtay/ATAforecasting/issues License: GPL (≥ 3) URL: https://github.com/alsabtay/ATAforecasting, https://atamethod.wordpress.com/ NeedsCompilation: yes Materials: README CRAN checks: ATAforecasting results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=ATAforecasting to link to this page.
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