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.

This package provides functions for 1D and 2D Discrete
        Cosine Transform (DCT), Discrete Sine Transform (DST) and
        Discrete Hartley Transform (DHT).

Manipulates date ('Date'), date time ('POSIXct') and time
    ('hms') vectors.  Date/times are considered discrete and are floored
    whenever encountered.  Times are wrapped and time zones are maintained
    unless explicitly altered by the user.

High-frequency time-series support via 'nanotime' and 'data.table'.

A comprehensive implementation of dynamic time warping
    (DTW) algorithms in R.  DTW computes the optimal (least cumulative
    distance) alignment between points of two time series.  Common DTW
    variants covered include local (slope) and global (window)
    constraints, subsequence matches, arbitrary distance definitions,
    normalizations, minimum variance matching, and so on.  Provides
    cumulative distances, alignments, specialized plot styles, etc.,
    as described in Giorgino (2009) <doi:10.18637/jss.v031.i07>.

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).

Time series clustering along with optimized techniques related
    to the Dynamic Time Warping distance and its corresponding lower bounds.
    Implementations of partitional, hierarchical, fuzzy, k-Shape and TADPole
    clustering are available. Functionality can be easily extended with
    custom distance measures and centroid definitions. Implementations of
    DTW barycenter averaging, a distance based on global alignment kernels,
    and the soft-DTW distance and centroid routines are also provided. 
    All included distance functions have custom loops optimized for the 
    calculation of cross-distance matrices, including parallelization support.
    Several cluster validity indices are included.