Authors@R: person("Peter", "Laurinec", email = "tsreprpackage@gmail.com",
Description: Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining.
Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also min-max and z-score normalisations, and forecasting accuracy measures are implemented.
Imports: Rcpp (>= 0.12.12), MASS, quantreg, wavelets, mgcv, dtt
Suggests: knitr, rmarkdown, ggplot2, data.table, moments

Suggests: knitr, rmarkdown, ggplot2, data.table, moments, testthat
Author: Peter Laurinec [aut, cre] (<https://orcid.org/0000-0002-3501-8783>)

Packaged: 2018-11-22 10:49:49 UTC; PeterLaurinec

Packaged: 2019-05-31 09:35:42 UTC; PeterLaurinec

Packaged: 2020-03-25 18:53:52 UTC; PeterLaurinec

Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson),
and forecasting accuracy measures are implemented.
Packaged: 2020-07-12 21:59:54 UTC; PeterLaurinec

Title: 'TSdbi' Extension to Connect with 'SDMX'
retrieve data in the Statistical Data and Metadata Exchange ('SDMX') format
EuroStat, the European Central Bank, the Organisation for Economic
Co-operation and Development, the Unesco Institute for Statistics,
and the International Labor Organization.)
Imports: methods, DBI (>= 0.3.1), tframe, tframePlus, RJSDMX(>= 1.3),

'EuroStat', the European Central Bank, the Organisation for Economic
Co-operation and Development, the 'Unesco' Institute for Statistics,

Title: Trend Estimation of Univariate and Bivariate Time Series with
person("L. Leticia", "Ramirez-Ramirez", email = "leticia.ramirez@cimat.mx", role = c("aut", "cre")),
person("Alejandro", "Islas-Camargo", email = "", role = "aut"),
person("Victor M.", "Guerrero", email = "", role = "aut"))
Description: It performs the smoothing approach provided by penalized least squares for univariate and bivariate time series, as proposed by Guerrero (2007) and Gerrero et al. (2017).
This allows to estimate the time series trend by controlling the amount of resulting (joint) smoothness.
Author: L. Leticia Ramirez-Ramirez [aut, cre],
Maintainer: L. Leticia Ramirez-Ramirez <leticia.ramirez@cimat.mx>