A 'PostgreSQL' interface for 'TSdbi'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
methods, DBI (>= 0.3.1), tframe, tframePlus, TSsql (>=
2015.1-1), RPostgreSQL

Functions for Baseline-Based Time Series Prediction
c(person(c("Rebecca", "Pontes"),"Salles", role = c("aut", "cre","cph"),email = "rebeccapsalles@gmail.com", comment = "CEFET/RJ"),person("Eduardo","Ogasawara", role = c("ths"),email = "eogasawara@ieee.org", comment = "CEFET/RJ"))
Functions for time series prediction and accuracy assessment using automatic ARIMA modelling. The generated ARIMA models and its yielded prediction errors are intended to be used as baseline for evaluating the practical value of other time series prediction methods and creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ),
Eduardo Ogasawara [ths] (CEFET/RJ)

Functions for Benchmarking Time Series Prediction
c(person(c("Rebecca", "Pontes"),"Salles", role = c("aut", "cre","cph"),email = "rebeccapsalles@acm.org", comment = "CEFET/RJ"),
person("Eduardo","Ogasawara", role = c("ths"),email = "eogasawara@ieee.org", comment = "CEFET/RJ"))
Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ), Eduardo Ogasawara [ths] (CEFET/RJ)
Functions for time series prediction and accuracy assessment using automatic linear modelling. The generated linear models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.

Functions for time series preprocessing, decomposition, prediction and accuracy assessment using automatic linear modelling. The generated linear models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
forecast, KFAS, stats, MuMIn, EMD, wavelets, vars