## Synopsis
Automatic Time Series Analysis and Forecasting using Ata Method with Box-Cox Power Transformations Family and Seasonal Decomposition Techniques.
Description
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
Installation
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")Example
USAccDeaths: Accidental Deaths in the US 1973–1978
library(ATAforecasting)
ATA(USAccDeaths, h = 18, model.type = "A", seasonal.type = "A", seasonal.model = "stl")Links
Github - Fable Modelling Wrappers for ATAforecasting Package
Github.io - Fable Modelling Wrappers for ATAforecasting Package
Optional Dependencies
ATAforecasting provides multiple seasonal decomposition methods:
Always available: - decomp - Classical decomposition (stats package) - stl - STL decomposition (stats/forecast packages) - stlplus - Enhanced STL (stlplus package) - tbats - TBATS model (forecast package) - stR - Regression-based STR (stR package)
Optional (requires additional installation): - x13 - X-13ARIMA-SEATS (requires: seasonal, x13binary) - x11 - X11 decomposition (requires: seasonal, x13binary)
To use X13/X11 methods:
install.packages(c("seasonal", "x13binary"))**Note:** x13binary contains software from the U.S. Census Bureau with
[special licensing terms](https://github.com/x13org/x13binary).
Users outside the United States should review the license before installation.Optional Dependencies
The x13 and x11 seasonal decomposition features optionally use x13binary, which contains X-13ARIMA-SEATS software from the U.S. Census Bureau.
The X-13ARIMA-SEATS software is not subject to copyright in the United States but has special licensing terms for use outside the U.S.
Users of x13/x11 features should review the licensing terms at: https://github.com/x13org/x13binary
All other features of ATAforecasting do not depend on x13binary.
As stated in the manual of X-13ARIMA-SEATS (June 12, 2023):
This Software was created by U.S. Government employees and therefore is not subject to copyright in the United States (17 U.S.C. §105). The United States/U.S. Department of Commerce (“Commerce”) reserve all rights to seek and obtain copyright protection in countries other than the United States. The United States/Commerce hereby grant to User a royalty-free, nonexclusive license to use, copy, and create derivative works of the Software outside of the United States.
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