Searches for additive outliers using the method described in Appendix C of Findley et al. (1998). If the number of trigonometric variables is not specified will search automatically through the model space to identify the best number of trigonometric variables, with the lowest AIC, AICc or BIC value.
x: Numeric vector. Time series to seasonally adjust
dates: a vector of class "Date", containing the data dates
out.tolerance: t-stat threshold for outliers (see Findley et al., 1998)
my.AO.list: (optional) Vector with user defined additive outlier variables
H: (optional) Matrix with holiday and trading day variables
my.k_l: (optional) Vector with the number of fourier terms to capture the yearly and monthly cycle. If NULL, would perform automatic search using AICc criterion
method: Decomposition method: "additive" or "multiplicative". By default uses the additive method
Returns
my.k_l
ao list of AO dates
Examples
#Not run:# Searching for additive outliers in Gasoline datadata(gasoline.data)ao_list=find_outliers(x=gasoline.data$y,dates = gasoline.data$date)
References
Findley, D.F., Monsell, B.C., Bell, W.R., Otto, M.C. and B.C Chen (1998). New capabilities and methods of the X-12-ARIMA seasonal-adjustment program. Journal of Business & Economic Statistics, 16(2), pp.127-152.