person("Peter", "Laurinec", email = "tsreprpackage@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-3501-8783"))
knitr, rmarkdown, ggplot2, data.table, moments, testthat
Peter Laurinec [aut, cre] (<https://orcid.org/0000-0002-3501-8783>)

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 various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson),
and forecasting accuracy measures are implemented.

'TSdbi' Extension to Connect with 'SDMX'
Methods to
retrieve data in the Statistical Data and Metadata Exchange ('SDMX') format
from several database. (For example,
EuroStat, the European Central Bank, the Organisation for Economic
Co-operation and Development, the Unesco Institute for Statistics,
and the International Labor Organization.)
This is a wrapper for package 'RJSDMX'.
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, RJSDMX(>= 1.3),
rJava

Methods to
retrieve data in the Statistical Data and Metadata Exchange ('SDMX') format
from several database. (For example,
'EuroStat', the European Central Bank, the Organisation for Economic
Co-operation and Development, the 'Unesco' Institute for Statistics,
and the International Labor Organization.)
This is a wrapper for package 'RJSDMX'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.

Trend Estimation of Univariate and Bivariate Time Series with
Controlled Smoothness
c(
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"))
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.
---
Guerrero, V.M (2007)  <DOI:10.1016/j.spl.2007.03.006>.
Guerrero, V.M; Islas-Camargo, A. and Ramirez-Ramirez, L.L. (2017) <DOI:10.1080/03610926.2015.1133826>.
L. Leticia Ramirez-Ramirez [aut, cre],
Alejandro Islas-Camargo [aut],
Victor M. Guerrero [aut]