ATA.Decomposition: Seasonal Decomposition for The ATAforecasting

In ATAforecasting: Automatic Time Series Analysis and Forecasting using the Ata Method

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 ts or msts or numeric object. if it is numeric object, findPeriod must be 1 or 2 or 3 or 4. if it is msts object, findPeriod must be 3 or 4.
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. none : seasonal decomposition is not required. decomp : classical seasonal decomposition. If decomp, the stats package will be used. stl : seasonal-trend decomposition procedure based on loess developed by Cleveland et al. (1990). If stl, the stats and forecast packages will be used. Multiple seasonal periods are allowed. stlplus : seasonal-trend decomposition procedure based on loess developed by Cleveland et al. (1990). If stlplus, the stlplus package will be used. tbats : exponential smoothing state space model with Box–Cox transformation, ARMA errors, trend and seasonal components. as described in De Livera, Hyndman & Snyder (2011). Parallel processing is used by default to speed up the computations. If tbats, the forecast package will be used. Multiple seasonal periods are allowed. stR : seasonal-trend decomposition procedure based on regression developed by Dokumentov and Hyndman (2015). If stR, the stR package will be used. Multiple seasonal periods are allowed. x13 : seasonal-trend decomposition procedure based on X13ARIMA/SEATS. If x13, the seasonal package will be used. x11 : seasonal-trend decomposition procedure based on X11. If x11, the seasonal package will be used.
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 s.frequency is not integer, X must be msts time series object. c(s1,s2,s3,...) for multiple period. If X has multiple periodicity, "tbats" or "stR" seasonal model have to be selected.
seas_attr_set	Assign from ATA.SeasAttr function. Attributes set for unit root and seasonality tests. For example: period of the input data which have one seasonal pattern –> 12 for monthly / 4 for quarterly / 7 for daily / 5 for business days. periods of the input data which have complex/multiple seasonal patterns –> c(7,354.37,365.25).

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

#'\insertRefshishkin1967ATAforecasting

#'\insertRefdagum1988ATAforecasting

#'\insertRefcleveland1990stlATAforecasting

#'\insertRefhafen2010localATAforecasting

#'\insertRefdelivera2011ATAforecasting

#'\insertRefdokumentov2015ATAforecasting

#'\insertRefdokumentov2020strATAforecasting

#'\insertRefmonsell2003towardATAforecasting

#'\insertRefmonsell2007xATAforecasting

#'\insertRefartseasonal2018ATAforecasting

See Also

stl, decompose, seas, tbats, stlplus, AutoSTR.

ATAforecasting documentation built on July 9, 2023, 7 p.m.