timeseriesdb: Manage and Archive Time Series Data in Establishment Statistics with R and PostgreSQL

KOF Working Paper No. 384

23 Pages Posted: 13 Jun 2015

See all articles by Matthias Bannert

Matthias Bannert

ETH Zürich - KOF Swiss Economic Institute

Date Written: June 10, 2015

Abstract

timeseriesdb is an R package which suggests a PostgreSQL database structure to store time series alongside extensive multi-lingual meta information and provides an R database interface including a web based GUI. The timeseriesdb package was designed to handle time series in establishment statistics. Information such as the GDP or data stemming from the aggregation of economic surveys is typically published on a monthly, quarterly or yearly basis. Hence the package is optimized to handle a large amount of different time series as opposed to managing a smaller number of high frequency time series such as real time data obtained from measuring devices. The particular focus of timeseriesdb is to help the user find and extract a particular set of information within a larger set of information. The timeseriesdb package intends to provide the infrastructure for a time series catalog as opposed to handling time series operations on database level. The underlying structure relies on PostgreSQL's hstore data type which allows to store an array of key-value pairs in a single cell. The hstore data type is not only used to reduce the number of records by storing an entire time series in a single record but also to store a record specific amount of multi-lingual meta information items flexibly.

Keywords: time series, data management, relational database, establishment statistics, official statistics, hstore, NoSQL, economic data, reproducible research, R, PostgreSQL

JEL Classification: C8

Suggested Citation

Bannert, Matthias, timeseriesdb: Manage and Archive Time Series Data in Establishment Statistics with R and PostgreSQL (June 10, 2015). KOF Working Paper No. 384, Available at SSRN: https://ssrn.com/abstract=2617582 or http://dx.doi.org/10.2139/ssrn.2617582

Matthias Bannert (Contact Author)

ETH Zürich - KOF Swiss Economic Institute ( email )

Zurich
Switzerland

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