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

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in KOF Swiss Economic Institute, ETH ZurichAbstract 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.

Aboveground biomass stock and change estimation in Amazon rainforest using airborne light detection and ranging, multispectral data, and machine learning algorithms

“…In this test, two methods are statistically different when their average ranks differ by at least one critical difference value 46 . The TStools package 48 available in R obtained the results. Finally, the algorithm that obtained the lowest RMSE value analyzed the dynamics of AGB in the study area between 2014 and 2017.…”