Abstract
The X-12-ARIMA seasonal adjustment program of the US Census Bureau extracts the different components (mainly: seasonal component, trend component, outlier component and irregular component) of a monthly or quarterly time series. It is the state-of-the- art technology for seasonal adjustment used in many statistical offices. It is possible to include a moving holiday effect, a trading day effect and user-defined regressors, and additionally incorporates automatic outlier detection. The procedure makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. The original output from X-12-ARIMA is somehow static and it is not always an easy task for users to extract the required information for further processing. The R package x12 provides wrapper functions and an abstraction layer for batch processing of X-12-ARIMA. It allows summarizing, modifying and storing the output from X-12-ARIMA within a well-defined class-oriented implementation. On top of the class-oriented (command line) implementation the graphical user interface allows access to the R package x12 without requiring too much R knowledge. Users can interactively select additive outliers, level shifts and temporary changes and see the impact immediately. The provision of the powerful X-12-ARIMA seasonal adjustment program available directly from within R, as well as of the new facilities for marking outliers, batch processing and change tracking, makes the package a potent and functional tool.
Highlights
The decomposition of monthly or quarterly time series into trend, seasonal and irregular components is an important part of time series analysis
Two software products which are widely used by statistical offices and which are focused on seasonal adjustment methods are the X-12-ARIMA software and TRAMO/SEATS that have been developed by the Bank of Spain
The seasonal adjustment becomes interactive by the dynamic selection of outliers, the straightforward changing of parameters within the graphical user interface (GUI) and the possibility to load any results that have been fitted
Summary
The decomposition of monthly or quarterly time series into trend, seasonal and irregular components is an important part of time series analysis. The seasonal component of a time series is removed to make it easier to focus on other components and for easier interpretation of the time series
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