knitr::opts_chunk$set( collapse = TRUE, comment = "#>", results='hide' )
It is now possible to seasonally adjust multiple series in a single call to
This is done by using the built-in batch mode of X-13. It removes the need for loops or
lapply() in such cases, and finally brings one missing feature of X-13 to seasonal -- the composite spec.
Multiple adjustments can be performed by supplying multiple time series as an
library(seasonal) m <- seas(cbind(fdeaths, mdeaths), x11 = "") final(m)
This will perform two seasonal adjustments, one for
fdeaths and one for
mdeaths. X-13 spec-argument combinations can be applied in the usual way, such
x11 = "". Note that if entered that way, they will apply to both series.
As in a single series call, we can also use the
seas(cbind(fdeaths, mdeaths), list = list(x11 = ""))
It is possible to specify individual specs for each series, by encapsulating
specific spec lists in the
list argument. In the following,
adjusted by X-11 and
mdeaths by the default SEATS procedure. The length of
list must be equal to number of series.
seas( cbind(fdeaths, mdeaths), list = list( list(x11 = ""), list() ) )
We can even combine these ideas. The following turns off the
AIC test of the regression spec for both series (
regression.aictest = NULL) and uses X-11 to adjust
fdeaths and SEATS to adjust
seas( cbind(fdeaths, mdeaths), regression.aictest = NULL, list = list( list(x11 = ""), list() ) )
There are several ways of specifying multiple series. We have already seen how
objects can be used as an input. Alternatively, we can also use a list of single
This is convenient if series differ in length or frequency. With the tsbox package, you can create such lists of time series from any time series object. Let us assume your data is in a data frame:
library(tsbox) dta <- ts_c(mdeaths = ts_df(mdeaths), AirPassengers = ts_df(AirPassengers)) head(dta)
In order to seasonally adjust all series in the data frame, you can run:
Finally, you can specify the data directly in the list of lists:
seas( list = list( list(x = mdeaths, x11 = ""), list(x = fdeaths) ) )
X-13 ships with a batch mode that allows multiple adjustments in a single call
to X-13. This is now the default in seasonal (
multimode = "x13").
Alternatively, X-13 can be called for each series (
multimode = "R").
The results should be usually the same, but switching to
multimode = "R" may be useful for debugging:
seas(cbind(fdeaths, mdeaths), multimode = "x13") seas(cbind(fdeaths, mdeaths), multimode = "R")
multimode = "x13" is faster. The following comparison on a MacBook Pro shows
a modest speed gain, but bigger differences have been observed on other systems:
many <- rep(list(fdeaths), 100) system.time(seas(many, multimode = "x13")) # user system elapsed # 9.415 0.653 10.079 system.time(seas(many, multimode = "R")) # user system elapsed # 11.130 1.039 12.324
Support for the X-13 batch mode makes it finally possible to use the composite spec -- the one feature of X-13 that was missing in seasonal. Sometimes, one has to decide whether seasonal adjustment should be performed on a granular level, or on an aggregated level. The composite spec helps you to analyze the problem and to compare the direct and the indirect adjustment.
X-13 requires to define a
series.comptype for individual series. Usually, this
will be set as
series.comptype = "add".
composite argument is a list with an X-13 specification that is applied on
the aggregated series. Specification works identical as for other series in
seas(), including the application of the defaults. If you provide an empty
list, the usual defaults of
seas() are used.
A minimal composite call looks like this:
m_composite <- seas( cbind(mdeaths, fdeaths), composite = list(), series.comptype = "add" ) m_composite
You can verify that the composite refers to the total of
fdeaths by running:
ldeaths is the sum of
series() can be used to extract the output or series of the composite adjustment:
out(m_composite) series(m_composite, "composite.indseasadj"))
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