Automatic Time Series Analysis and Forecasting using Ata Method with Box-Cox Power Transformations Family and Seasonal Decomposition Techniques.
The Ata Method is a new alternative forecasting method. This method is alternative to two major forecasting approaches: Exponential Smoothing and ARIMA. The Ata method based on the modified simple exponential smoothing as described in Yapar, G. (2016) doi:10.15672/HJMS.201614320580, Yapar G., Capar, S., Selamlar, H. T., Yavuz, I. (2017) doi:10.15672/HJMS.2017.493 and Yapar G., Selamlar, H. T., Capar, S., Yavuz, I. (2019) doi:10.15672/hujms.461032 is a new univariate time series forecasting method which provides innovative solutions to issues faced during the initialization and optimization stages of existing 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 dat
You can install the stable version from CRAN:
install.packages("ATAforecasting")Development version with latest features:
devtools::install_github("alsabtay/ATAforecasting")Fable Modelling Wrappers for ATAforecasting Package
devtools::install_github("alsabtay/fable.ata")USAccDeaths: Accidental Deaths in the US 1973–1978
library(ATAforecasting)
ATA(USAccDeaths, h = 18, model.type = "A", seasonal.type = "A", seasonal.model = "stl")Github - Fable Modelling Wrappers for ATAforecasting Package
Github.io - Fable Modelling Wrappers for ATAforecasting Package
This package is free and open source software, licensed under GPL-3.