Imports: methods, TSdbi (>= 2009.1-1), DBI, RPostgreSQL
Packaged: Sat Mar 21 04:58:36 2009; rforge

Packaged: Wed Mar 25 05:05:59 2009; rforge

Imports: methods, TSdbi (>= 2009.5-1), DBI, RPostgreSQL

Imports: methods, TSdbi (>= 2010.3-1), DBI, RPostgreSQL

Imports: methods, TSdbi (>= 2011.6-1), DBI, RPostgreSQL

Suggests: zoo, tseries, TShistQuote, tfplot

Depends: R (>= 2.8.0), TSdbi (>= 2013.9-1), RPostgreSQL
Imports: methods, DBI, tframe, tframePlus, TSsql (>= 2013.9-1)

Title: 'TSdbi' Extensions for 'PostgreSQL'
Description: A 'PostgreSQL' interface for 'TSdbi'.
Imports: methods, DBI (>= 0.3.1), tframe, tframePlus, TSsql (>=

Title: Functions for Baseline-Based Time Series Prediction
Authors@R: 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"))
Maintainer: Rebecca Pontes Salles <rebeccapsalles@gmail.com>
Description: 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.
Author: Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ),

Title: Functions for Benchmarking Time Series Prediction
Authors@R: 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"))
Author: Rebecca Pontes Salles [aut, cre, cph] (CEFET/RJ), Eduardo Ogasawara [ths] (CEFET/RJ)
Maintainer: Rebecca Pontes Salles <rebeccapsalles@acm.org>
Description: 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.