Package: ATAforecasting 0.0.60

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.

Authors:Ali Sabri Taylan [aut, cre, cph], Hanife Taylan Selamlar [aut, cph], Guckan Yapar [aut, ths, cph]

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Bug tracker:https://github.com/alsabtay/ataforecasting/issues

On CRAN:

ataataforecastingfableforecastforecastingtime-series

18 exports 4 stars 1.29 score 63 dependencies 1 dependents 4 scripts 653 downloads

Last updated 1 years agofrom:c292e006cd. Checks:OK: 7 NOTE: 2. Indexed: yes.

Exports:ATAATA.AccuracyATA.BackTransformATA.BoxCoxAttrATA.CIATA.CoreATA.DecompositionATA.ForecastATA.PlotATA.PrintATA.SeasAttrATA.SeasonalityATA.ShiftATA.Shift_MatATA.Transformfind.freqfind.freq.fourierfind.multi.freq

Dependencies:clicodetoolscolorspacecurlfansifarverforeachforecastfracdiffgenericsggplot2gluegtableisobanditeratorsjsonlitelabelinglatticeleapslifecyclelmtestlocfitmagrittrMASSMatrixMatrixModelsmgcvmunsellnlmennetpillarpkgconfigquadprogquantmodquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrlangscalesseasonalSparseMstlplusstRsurvivaltibbletimeDatetimeSeriesTSAtseriesTTRurcautf8vctrsviridisLitewithrx13binaryxtsyaImputezoo