JDemetra+ is an open source, platform independent, extensible software for seasonal adjustment (SA) and other related time series problems developed by the National Bank of Belgium in cooperation with the Deutsche Bundesbank and Eurostat1.
JDemetra+ consists of several software components, which can be used independently in other applications. In addition to the graphical interface, there is also a command line tool for batch SA processing (called the “Cruncher”), and extensions for use with the “R” language. There is also a dedicated “R” package that can be used as an alternative to the graphical interface.
JDemetra+ implements the concepts and algorithms used in the two leading SA methods: TRAMO-SEATS+2 and X-12ARIMA/X-13ARIMA-SEATS3. These methods have been re-engineered using an object-oriented approach that enables easier handling, extensions and modifications.
The program TRAMO-SEATS+ was developed by Gianluca Caporello and Agustin Maravall - with programming support from Domingo Perez and Roberto Lopez - at the Bank of Spain. It is based on the program TRAMO-SEATS, previously developed by Victor Gomez and Agustin Maravall. The program X-13ARIMA-SEATS is a produced, distributed, and maintained by the US Census Bureau.
Besides seasonal adjustment, JDemetra+ bundles other time series models that are useful in the production or analysis of economic statistics, including for instance outlier detection, nowcasting, temporal disaggregation and benchmarking.
JDemetra+ enables the implementation of the ‘ESS Guidelines on Seasonal Adjustment’ (2015). JDemetra+ has been officially recommended, as of 2 February 2015, to the members of the ESS and the European System of Central Banks as software for seasonal and calendar adjustment of official statistics.
From a technical point of view, JDemetra+ is a collection of reusable and extensible Java components, which can be easily accessed through a rich graphical interface. The software is a free and open-source(FOSS) developed under the EUPL licence.
Seasonal adjustment (SA) is an important component of the official statistics business process. This technique is widely used for estimating and removing seasonal and calendar-related movements from time series resulting in data that present a clear picture of economic phenomena. Therefore, Eurostat takes part in various activities that aim to promote, develop and maintain a publicly available software solution for SA in line with established best practices.
Among many seasonal adjustment methods that produce reliable results for large datasets the most widely used and recommended ones are X-12-ARIMA4/X-13ARIMA-SEATS developed at the U.S. Census Bureau and TRAMO-SEATS developed by Victor Gómez and Agustín Maravall, from the Banco de España. Both methods are divided into two main parts. The first part is called a pre-adjustment. It removes various deterministic effects from the series by means of a regression model with ARIMA5 noise. The second part is the decomposition of the time series that aims to estimate and remove a seasonal component from the time series. TRAMO-SEATS and X-12-ARIMA/X-13ARIMA-SEATS use a very similar approach in the first part to estimate the same model on the processing step, but they differ completely in the decomposition step. Therefore, comparing results from decomposition is often difficult. Furthermore, their diagnostics focus on different aspects and their outputs take completely different forms.
The TRAMO-SEATS method was originally implemented in 2001 in the program TSW (Tramo-Seats-Windows), which is a Windows extension of programs TRAMO and SEATS. Since then, a considerable amount of changes and additions have been added, that affect many important input parameters, as well as the output obtained. These changes resulted in program TSW+6 launched in 2014. A LINUX version of TRAMO-SEATS is also available.
For X-13ARIMA-SEATS the U.S. Census Bureau provides the Windows interface called Win X-13.7 Distributions of X-13ARIMA-SEATS for Linux and Unix platforms are also available.8
Both the above seasonal adjustment programs were originally written in FORTRAN, which is currently recognized as a declining language. The FORTRAN limitations – especially for the creation of reusable components and for the management of complex problems – make the maintenance of the relevant IT codes increasingly burdensome.
These original seasonal adjustment programs are commonly perceived by users as difficult to operate. Therefore, to improve access to these SA methods for non-specialists, Eurostat introduced new software called Demetra. It offers a user-friendly interface to the two SA algorithms: TRAMO-SEATS and X-12-ARIMA and facilitated the comparison of the output from those two algorithms. Even so, Demetra uses the FORTRAN libraries, which, together with an insufficient product development and handling of errors, is a factor that caused a rapid decline in software’s usage.
In 2009, the European Statistical System (ESS) launched its ‘Guidelines on Seasonal Adjustment’ 9. As Demetra could not be adapted to the new requirements in the Guidelines, Eurostat, in cooperation with the National Bank of Belgium (NBB), started a project aiming to develop improved software called Demetra+. It was released in 2012. This tool provides a common approach for seasonal adjustment using the TRAMO-SEATS and X-12-ARIMA methods, which is more coherent with the Guidelines. It includes a unified graphical interface and input/output diagnostics for the two methods. Demetra+ source code is written in C++ and uses the two original FORTRAN modules, as well as .NET libraries. Therefore Demetra+ software is non-extensible and cannot be used in IT environments other than Windows. For these reasons it seems that in long-term perspective it will not meet users’ expectations.
Therefore, Eurostat took an initiative to create new software that is based on Demetra+ experience but is platform independent and extensible. The resulting program is called JDemetra+. The NBB has been developing it since 2012. From the typical user perspective in comparison with Demetra+, numerous improvements have been implemented in JDemetra+, in terms of both layout and functionalities. The most critical innovation is the re-writing of the original FORTRAN codes of X-12-ARIMA/X-13ARIMA-SEATS and TRAMO-SEATS in JAVA, following a real object-oriented approach. These functionalities are discussed in the next section.
JDemetra+ offers up-to-date versions of leading seasonal adjustment algorithms rewritten in Java, which is a crucial factor that enables the long-term maintenance of the tool, integration of the libraries in the IT environments of many institutions and reuse of the modules and algorithms for other purposes. JDemetra+ is not only a user-friendly graphical interface, comparable to its predecessor, Demetra+, but also a set of open Java libraries that can be used to deal with time series related issues including the SA processing of large-scale datasets, the use of non-standard SA methods, the development of advanced research modules, temporal disaggregation, benchmarking and business cycle analysis.
JDemetra+ is built around the concepts and the algorithms used in the two leading SA methods, i.e. TRAMO-SEATS and X-12-ARIMA/X-13ARIMA-SEATS. They have been re-engineered, following an object-oriented approach, which allows easier handling, extensions or modifications. One of the strategic choices for JDemetra+ is to provide common presentation/analysis tools for the seasonal adjustment methods included, so that the results from different methods can easily be compared. Obviously, JDemetra+ is highly influenced by the output of TRAMO-SEATS and of X-12-ARIMA/X-13ARIMA-SEATS. Most analyses implemented in JDemetra+ are available in the core engines. However, the results produced by JDemetra+ may slightly differ for several reasons (different statistical/algorithmic choices). In any case the global messages from seasonal adjustment are (nearly) always similar. Among numerous important tools implemented in JDemetra+, the following functionalities should be highlighted:
JDemetra+ is written using object-oriented programming (OOP) methodology. It allows developers to design software in a modular way, i.e. separate the functionality of an application into independent, interchangeable modules. Such units provide a specific group of functionalities and can be detached from the whole concept. The object-oriented approach is especially useful in the case of complex programs or when re-usability matters. Beside the statistical algorithms, JDemetra+ provides numerous peripheral services. The most important ones are the following:
As mentioned above, the API could be used to generate completely independent applications, but also to create, more easily, extensions to the current application. One of the aims of JDemetra+ was to develop software that enables the comparison of the result from TRAMO-SEATS and X-12-ARIMA/X-13ARIMA-SEATS. For this reason, most of the analysis tools are common to both algorithms, e.g. the revision history and the sliding spans, even if they were originally developed in only one of them. On the other hand, all the features developed in the original programs have not always been implemented in JDemetra+; for instance, by contrast with TRAMO-SEATS, JDemetra+ does not separate the long-term trend from the cycle. JDemetra+ runs on operating systems that support the Java VM (Virtual Machine) such as:
Eurostat is the statistical office of the European Union. Its task is to provide the European Union with statistics at European level that enable comparisons between countries and regions. ↩
TRAMO-SEATS is a model-based seasonal adjustment method developed by Victor Gómez and Agustin Maravall (the Banco de España). It consists of two linked programs: TRAMO and SEATS. TRAMO (“Time Series Regression with ARIMA Noise, Missing Observations, and Outliers”) performs estimation, forecasting, and interpolation of regression models with missing observations and ARIMA errors, in the presence of possibly several types of outlier. SEATS (“Signal Extraction in ARIMA Time Series”) performs an ARIMA-based decomposition of an observed time series into unobserved components. Both programs are supported by the Banco de España. For basic information on the TRAMO-SEATS see CAPORELLO, G., and MARAVALL, A. (2004). More information on TRAMO-SEATS can be found at www.bde.es. ↩
X-13ARIMA-SEATS is a seasonal adjustment program developed and supported by the U.S. Census Bureau that contains two seasonal adjustment modules: the enhanced X-11 seasonal adjustment procedure and an ARIMA model based seasonal adjustment procedure from the SEATS seasonal adjustment program developed by GÓMEZ, V., and MARAVALL, A. (2013). For information on the X-13ARIMA-SEATS program see ‘X-13ARIMA-SEATS Reference Manual’ (2015). More information on X-13ARIMA-SEATS can be found at U.S. Census Bureau webpage. ↩
X-12-ARIMA is a seasonal adjustment program developed and supported by the U.S. Census Bureau. It includes all the capabilities of the X-11 program (see DAGUM, E.B. (1980)), which estimates trend and seasonal component using moving averages. X-12-ARIMA offers useful enhancements including: extension of the time series with forecasts and backcasts from the ARIMA models prior to seasonal adjustment, adjustment for effects estimated with user-defined regressors, additional seasonal and trend filter options, alternative seasonal-trend-irregular decomposition, additional diagnostics of the quality and stability of the adjustments, extensive time series modelling and model selection capabilities for linear regression models with ARIMA errors. For basic information on the X-12-ARIMA program see ‘X-12-ARIMA Reference Manual’ (2011). More information on X-12-ARIMA can be found at a dedicated U.S. Census Bureau webpage. ↩
For description of the ARIMA model see the Linearisation section. ↩
MARAVALL, A., CAPORELLO, G., PÉREZ, D., and LÓPEZ, R. (2014). ↩
Documentation on Win X-X13 can be found at a dedicated U.S. Census Bureau webpage. ↩
Endorsed by the Statistical Programme Committee, the European Statistical System (ESS) ‘Guidelines on Seasonal Adjustment’ (2009) aim to harmonize European practices and to improve the comparability of infra-annual national statistics as well as enhance the overall quality of the European Union and the euro area aggregates. The ‘ESS Guidelines on Seasonal Adjustment’ (2009) and its revised version released in 2015 cover all the key steps of the seasonal and calendar adjustment process. They discuss both the theoretical aspects and practical implementation of seasonal adjustment issues. ↩