Basic time series functionalities such as listing of missing
values, application of arbitrary aggregation as well as rolling (asymmetric)
window functions and automatic detection of periodicity. As it is mainly
based on 'data.table', it is fast and - in combination with the 'R6'
package - offers reference semantics. In addition to its native R6
interface, it provides an S3 interface inclusive an S3 wrapper method
generator for those who prefer the latter. Finally yet importantly, its
functional approach allows incorporating functionalities from many other
packages.
covr, dygraphs, fasttime, knitr, magrittr, RColorBrewer,
rmarkdown, runner (>= 0.3.5), tinytest

A Class for Working with Time Series Data Based on 'data.table'
and 'R6' with Largely Optional Reference Semantics
Basic time series functionalities such as listing of missing
values, application of arbitrary aggregation as well as rolling (asymmetric)
window functions and automatic detection of periodicity. As it is mainly
based on 'data.table', it is fast and - in combination with the 'R6'
package - offers reference semantics. In addition to its native R6
interface, it provides an S3 interface inclusive an S3 wrapper method
generator for those who prefer the latter. Finally yet importantly, its
functional approach allows for incorporating functionalities from many other
packages.

Basic time series functionalities such as listing of missing
values, application of arbitrary aggregation as well as rolling (asymmetric)
window functions and automatic detection of periodicity. As it is mainly
based on 'data.table', it is fast and - in combination with the 'R6'
package - offers reference semantics. In addition to its native R6
interface, it provides an S3 interface for those who prefer the latter.
Finally yet importantly, its functional approach allows for incorporating
functionalities from many other packages.
dygraphs, fasttime, knitr, magrittr, RColorBrewer, RcppCCTZ,
rmarkdown, runner (>= 0.3.5), tinytest, units

Imputation of Time Series Based on Dynamic Time Warping
DEZECACHE Camille, PHAN Thi Thu Hong, POISSON-CAILLAULT Emilie
Functions to impute large gaps within time series based on Dynamic Time Warping methods. It contains all required functions to create large missing consecutive values within time series and to fill them, according to the paper Phan et al. (2017), <DOI:10.1016/j.patrec.2017.08.019>. Performance criteria are added to compare similarity between two signals (query and reference).