generator for those who prefer the latter. Finally yet importantly, its
functional approach allows incorporating functionalities from many other

Title: A Class for Working with Time Series Data Based on 'data.table'
and 'R6' with Largely Optional Reference Semantics
functional approach allows for incorporating functionalities from many other

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.
Suggests: dygraphs, fasttime, knitr, magrittr, RColorBrewer, RcppCCTZ,
rmarkdown, runner (>= 0.3.5), tinytest, units

Title: Imputation of Time Series Based on Dynamic Time Warping
Author: DEZECACHE Camille, PHAN Thi Thu Hong, POISSON-CAILLAULT Emilie
Description: 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).
Imports: dtw, rlist, stats, e1071, entropy, lsa

Author: Camille Dezecache, T. T. Hong Phan, Emilie Poisson-Caillault

Title: Imputation of Multivariate Time Series Based on Dynamic Time
Description: Functions to impute large gaps within multivariate time series based on Dynamic Time Warping methods. Gaps of size 1 or inferior to a defined threshold are filled using simple average and weighted moving average respectively. Larger gaps are filled using the methodology provided by Phan et al. (2017) <DOI:10.1109/MLSP.2017.8168165>: a query is built immediately before/after a gap and a moving window is used to find the most similar sequence to this query using Dynamic Time Warping. To lower the calculation time, similar sequences are pre-selected using global features. Contrary to the univariate method (package 'DTWBI'), these global features are not estimated over the sequence containing the gap(s), but a feature matrix is built to summarize general features of the whole multivariate signal. Once the most similar sequence to the query has been identified, the adjacent sequence to this window is used to fill the gap considered. This function can deal with multiple gaps over all the sequences componing the input multivariate signal. However, for better consistency, large gaps at the same location over all sequences should be avoided.
Imports: dtw, rlist, stats, e1071, entropy, lsa, DTWBI