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• X-13arima
• X-13arima-seats

x13binary 1.1.39-3 on CRAN: (Imperfect) Package Updates

A new release 1.1.39-3 of x13binary, of the X-13ARIMA-SEATS program by the US Census Bureau (with upstream release 1.1.39) is now on CRAN.

Executable binary （実行. The US Census Bureau provides a seasonal adjustment program now called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions - which this package integrates for use by other R packages. This procedure performs a seasonal adjustment of time series data using the procedure currently employed by the United States Census Bureau. As part of the procedure, the time series is decomposed into 3 components: trend-cycle, seasonality, and randomness. DC-Unlocker 1.00.1422 Crack Android + Mac DC-Unlocker Crack is the new software that unlocks phones, modems, and routers. By using this tool, you can easily unlock all of the models’ internal and external modem without any effort and cost.

The x13binary package takes the pain out of installing X-13ARIMA-SEATS by making it a fully resolved CRAN dependency. For example, when installing the excellent seasonal package by Christoph, then X-13ARIMA-SEATS will get pulled in via the x13binary package and things just work. Just depend on x13binary and on all major 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. With this 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 17350+ CRAN packages. You can read more about this (and the seasonal package) in the Journal of Statistical Software paper by Christoph and myself.

This release was needed because the recent M1mac build was reporting leftover ‘detritus’ in the temporary directory, which we addressed with an explicit removal at end. We also addressed another CRAN Policy change since the last release, namely a conversion of the configure script from bash to sh.

X-13arima

Now, sadly, that second aspect blew up on Solaris, and the ‘detritus’ issue appears to be persist. By now Christoph and a colleague have installed R(-devel) on such an M1 machine, but still cannot reproduce. We will reach out to CRAN to learn more. A follow-up release 1.1.39-4 is likely.

The good news is that the standard macOS binary works on M1 as do other binaries thanks to the translation layer. We do however lack a genuine binary for Solaris so if any of the esteemed readers of this post happens to have access to R on Solaris along with a basic Fortran compiler, we would love to hear from you. Building X-13ARIMA-SEATS from source on Solaris should be straightforward, it is on the other OSs.

X-13arima-seats

Courtesy of my CRANberries, there is also a diffstat report for this release showing changes to the previous release.

If you like this or other open-source work I do, you can sponsor me at GitHub.

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. Please report excessive re-aggregation in third-party for-profit settings.

/code/x13binary | permanent link

X-13ARIMA-SEATS, successor to X-12-ARIMA and X-11, is a set of statistical methods for seasonal adjustment and other descriptive analysis of time series data that are implemented in the U.S. Census Bureau's software package.^[3] These methods are or have been used by Statistics Canada, Australian Bureau of Statistics, and the statistical offices of many other countries.^[4][5]

X-12-ARIMA can be used together with many statistical packages, such as SAS in its econometric and time series (ETS) package, R in its (seasonal) package,^[6]Gretl or EViews which provides a graphical user interface for X-12-ARIMA, and NumXL which avails X-12-ARIMA functionality in Microsoft Excel.^[7] There is also a version for Matlab.^[8]

Notable statistical agencies presently using X-12-ARIMA for seasonal adjustment include Statistics Canada,^[9] the U.S. Bureau of Labor Statistics^[10] and Census and Statistics Department (Hong Kong).^[11] The Brazilian Institute of Geography and Statistics uses X-13-ARIMA.^[12]

X-12-ARIMA was the successor to X-11-ARIMA; the current version is X-13ARIMA-SEATS.^[13]

X-13-ARIMA-SEATS's source code can be found on the Census Bureau's website.^[14]

Methods[edit]

The default method for seasonal adjustment is based on the X-11 algorithm. It is assumed that the observations in a time series, ${\displaystyle Y_t}$, can be decomposed additively,

or multiplicatively,

In this decomposition, ${\displaystyle T_t}$ is the trend (or the 'trend cycle' because it also includes cyclical movements such as business cycles) component, ${\displaystyle S_t}$ is the seasonal component, and ${\displaystyle I_t}$ is the irregular (or random) component. The goal is to estimate each of the three components and then remove the seasonal component from the time series, producing a seasonally adjusted time series.^[15]

The decomposition is accomplished through the iterative application of centered moving averages. For an additive decomposition of a monthly time series, for example, the algorithm follows the following pattern:

1. An initial estimate of the trend is obtained by calculating centered moving averages for 13 observations (from ${\displaystyle t-6}$ to ${\displaystyle t+6}$).
2. Subtract the initial estimate of the trend series from the original series, leaving the seasonal and irregular components (SI).
3. Calculate an initial estimate of the seasonal component using a centered moving average of the SI series at seasonal frequencies, such as ${\displaystyle t-24,t-12,t,t+12,t+24}$
4. Calculate an initial seasonally adjusted series by subtracting the initial seasonal component from the original series.
5. Calculate another estimate of the trend using a different set of weights (known as 'Henderson weights').
6. Remove the trend again and calculate another estimate of the seasonal factor.
7. Seasonally adjust the series again with the new seasonal factors.
8. Calculate the final trend and irregular components from the seasonally adjusted series.

The method also includes a number of tests, diagnostics and other statistics for evaluating the quality of the seasonal adjustments.

See also[edit]

References[edit]

1. ^https://www.census.gov/srd/www/x13as/x13down_unix.html
2. ^https://www.census.gov/srd/www/disclaimer.html
3. ^'X-13ARIMA-SEATS Seasonal Adjustment Program'. United States Census Bureau. Retrieved March 24, 2021.
4. ^'Time Series Analysis: Seasonal Adjustment Methods'. November 14, 2005.
5. ^Susie Fortier and Guy Gellatly. 'Seasonally adjusted data – Frequently asked questions'. Retrieved March 24, 2021.CS1 maint: uses authors parameter (link)
6. ^'seasonal: R Interface to X-13-ARIMA-SEATS version 1.8.2 from CRAN'. rdrr.io. Retrieved 2021-05-25.
7. ^'Implementation of the X-11 Seasonal Adjustment Method'.
8. ^'X-13 Toolbox for Seasonal Filtering'. www.mathworks.com. Retrieved 2021-05-25.
9. ^http://www.statcan.gc.ca/pub/12-539-x/2009001/seasonal-saisonnal-eng.htm
10. ^http://www.bls.gov/cpi/cpisahoma.htm
11. ^https://www.censtatd.gov.hk/hkstat/sub/sc30.jsp
12. ^ftp://ftp.ibge.gov.br/Contas_Nacionais/Contas_Nacionais_Trimestrais/Ajuste_Sazonal/X13_NasContasTrimestrais.pdf
13. ^https://www.census.gov/srd/www/x13as/
14. ^https://www.census.gov/srd/www/x13as/x13down_unix.html
15. ^Findley, David F.; Monsell, Brian C.; Bell, William R.; Otto, Mark C.; Chen, Bor-Chung (1998), 'New Capabilities and Methods of the X-12-ARIMA Seasonal Adjustment Program'(PDF), Journal of Business and Economic Statistics, 16

External links[edit]