The x13binary package takes the pain out of installing X-13ARIMA-SEATS by making it a fully resolved CRAN dependency. For example, if you install the excellent seasonal package by Christoph, then X-13ARIMA-SEATS will get pulled in via the x13binary package and things just work: Depend on x13binary and on all relevant OSs supported by R, you should have an X-13ARIMA-SEATS binary installed which will be called seamlessly by the higher-level packages such as seasonal or gunsales.
So now the full power of the what is likely the world's most sophisticated deseasonalization and forecasting package is now at your fingertips and the R prompt, just like any other of the 10,500+ CRAN packages.
Not many packaging changes in this release besides updating the underlying builds, but we switched our versioning scheme to reflect that our releases are driven by the US Census Bureau releases. But thanks to an initial contribution by David Schaub we now support the 'armhf' architecture common on Chromebooks running Linux.
Our x13binary package takes the pain out of installing this, and provides it to the seasonal package upon which other packages such as gunsales or ggseas build their functionality. Things just work: Depend on x13binary and on all relevant OSs supported by R, you should have an X-13ARIMA-SEATS binary installed which will be called seamlessly by the higher-level packages. See this announcement blog post describing the initial 0.1.0 release; we had since followed up with a 0.1.1 release tightening up behaviour on two edge case OSs.
The seasonal package by Christoph Sax brings a very featureful and expressive interface for working with seasonal data to the R environment. It uses the standard tool of the trade: X-13ARIMA-SEATS. This powerful program is provided by the statisticians of the US Census Bureau based on their earlier work (named X-11 and X-12-ARIMA) as well as the TRAMO/SEATS program by the Bank of Spain. X-13ARIMA-SEATS is probably the best known tool for de-seasonalization of timeseries, and used by statistical offices around the world.
Sadly, it also has a steep learning curve. One interacts with a basic command-line tool which users have to download, install and properly reference (by environment variables or related means). Each model specification has to be prepared in a special 'spec' file that uses its own, cumbersome syntax.
As seasonal provides all the required functionality to use X-13ARIMA-SEATS from R --- see the very nice seasonal demo site --- it still required the user to manually deal with the X-13ARIMA-SEATS installation.
So we decided to do something about this. A pair of GitHub repositories provide both the underlying binary in a per-operating system form (see x13prebuilt) as well as a ready-to- use R package (see x13binary) which uses the former to provide binaries for R. And the latter is now on CRAN as package x13binary ready to be used on Windows, OS-X or Linux. And the seasonal package (in version 1.2.0 -- now on CRAN -- or later) automatically makes use of it. Installing seasaonal and x13binary in R is now as easy as:
which opens the door for effortless deployment of powerful deasonalization. By default, the principal function of the package employs a number of automated techniques that work well in most circumstances. For example, the following code produces a seasonal adjustment of the latest data of US retail sales (by the Census Bureau) downloaded from Quandl:
library(seasonal) library(Quandl) ## not needed for seasonal but has some niceties for Quandl data rs <- Quandl(code="USCENSUS/BI_MARTS_44000_SM", type="ts")/1e3 m1 <- seas(rs) plot(m1, main = "Retail Trade: U.S. Total Sales", ylab = "USD (in Billions)")
This tests for log-transformation, performs an automated ARIMA model search, applies outlier detection, tests and adjusts for trading day and easter effects, and invokes the SEATS method to perform seasonal adjustment. And this is how the adjusted series looks like:
Of course, you can access all available options of X-13ARIMA-SEATS as well. Here is an example where we adjust the latest data for Chinese exports (as tallied by the US FED), taking into account the different effects of Chinese New Year before, during and after the holiday:
xp <- Quandl(code="FRED/VALEXPCNM052N", type="ts")/1e9 m2 <- seas(window(xp, start = 2000), xreg = cbind(genhol(cny, start = -7, end = -1, center = "calendar"), genhol(cny, start = 0, end = 7, center = "calendar"), genhol(cny, start = 8, end = 21, center = "calendar") ), regression.aictest = c("td", "user"), regression.usertype = "holiday") plot(m2, main = "Goods, Value of Exports for China", ylab = "USD (in Billions)")
which generates the following chart demonstrating a recent flattening in export activity measured in USD.
We hope this simple examples illustrates both how powerful a tool
X-13ARIMA-SEATS is, but also just how easy it is to use X-13ARIMA-SEATS from R now that we provide the x13binary package automating its installation.