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 one-character 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 X-11 Variant of the Census-II Method Seasonal Adjustment Program.” Technical Report 15, Bureau of the U.S. Census. https://www.census.gov/content/dam/Census/library/working-papers/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/working-papers/1999/adrm/emanual.pdf.
#'Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990). “STL: A seasonal-trend 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 Seasonal-Trend 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:2015-13.
#'Dokumentov A, Hyndman RJ (2020). “STR: A Seasonal-Trend Decomposition Procedure Based on Regression.” 2009.05894.
#'Monsell BC, Aston JAD, Koopman SJ (2003). “Toward X-13?” United States Census Bureau. https://www.census.gov/content/dam/Census/library/working-papers/2003/adrm/jsm2003bcm.pdf.
#'Monsell BC (2007). “The X-13A-S seasonal adjustment program.” In Proceedings of the 2007 Federal Committee On Statistical Methodology Research Conference. URL http://www. fcsm. gov/07papers/Monsell. II-B. pdf.
#'Sax C, Eddelbuettel D (2018). “Seasonal Adjustment by X-13ARIMA-SEATS in R.” Journal of Statistical Software, 87(11), 1–17.
See Also
stl
, decompose
, seas
,
tbats
, stlplus
, AutoSTR
.