ATA.Decomposition {ATAforecasting}  R Documentation 
Seasonal Decomposition for The ATAforecasting
Description
Automatic seasonal decomposition for ATA Method is called ATA.Decomposition
function in ATAforecasting package.
The function returns seasonally adjusted data constructed by removing the seasonal component. The methodology is fully automatic.
The ATA.Decomposition
function works with many different types of inputs.
Usage
ATA.Decomposition(input, s.model, s.type, s.frequency, seas_attr_set)
Arguments
input 
It must be 
s.model 
A string identifying method for seasonal decomposition. If NULL, "decomp" method is default. c("none", "decomp", "stl", "stlplus", "tbats", "stR") phrases of methods denote.

s.type 
A onecharacter string identifying method for the seasonal component framework. If NULL, "M" is default. The letter "A" for additive model, the letter "M" for multiplicative model. 
s.frequency 
Value(s) of seasonal periodicity. If 
seas_attr_set 
Assign from 
Value
Seasonal components of the univariate time series.
ATA.Decomposition
is a list containing at least the following elements:
AdjustedX 
Deseasonalized data 
SeasIndex 
Particular weights of seasonality given cycle/frequency 
SeasActual 
Seasonality given original data 
SeasType 
Seasonal decomposition technique 
Author(s)
Ali Sabri Taylan and Hanife Taylan Selamlar
References
#'Shiskin J, Young AH, Musgrave JC (1967). “The X11 Variant of the CensusII Method Seasonal Adjustment Program.” Technical Report 15, Bureau of the U.S. Census. https://www.census.gov/content/dam/Census/library/workingpapers/1967/adrm/shiskinyoungmusgrave1967.pdf.
#'Dagum EB (1999). X11ARIMA/2000 An Updated of The X11ARIMA/88 Seasonal Adjustment Method  Foundations and Users' Manual. Statistics Canada. https://www.census.gov/content/dam/Census/library/workingpapers/1999/adrm/emanual.pdf.
#'Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990). “STL: A seasonaltrend decomposition procedure based on loess.” Journal of Official Statistics, 6(1), 3–73.
#'Hafen RP (2010). Local regression models: Advancements, applications, and new methods. Ph.D. thesis, Purdue University.
#'Livera AMD, Hyndman RJ, Snyder RD (2011). “Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing.” Journal of the American Statistical Association, 106(496), 1513–1527.
#'Dokumentov A, Hyndman RJ (2015). “STR: A SeasonalTrend Decomposition Procedure Based on Regression.” Monash Econometrics and Business Statistics Working Papers 13/15, Monash University, Department of Econometrics and Business Statistics. https://EconPapers.repec.org/RePEc:msh:ebswps:201513.
#'Dokumentov A, Hyndman RJ (2020). “STR: A SeasonalTrend Decomposition Procedure Based on Regression.” 2009.05894.
#'Monsell BC, Aston JAD, Koopman SJ (2003). “Toward X13?” United States Census Bureau. https://www.census.gov/content/dam/Census/library/workingpapers/2003/adrm/jsm2003bcm.pdf.
#'Monsell BC (2007). “The X13AS seasonal adjustment program.” In Proceedings of the 2007 Federal Committee On Statistical Methodology Research Conference. URL http://www. fcsm. gov/07papers/Monsell. IIB. pdf.
#'Sax C, Eddelbuettel D (2018). “Seasonal Adjustment by X13ARIMASEATS in R.” Journal of Statistical Software, 87(11), 1–17.
See Also
stl
, decompose
, seas
,
tbats
, stlplus
, AutoSTR
.