Package: timetk
Type: Package
Title: A Tool Kit for Working with Time Series
Version: 0.1.2
Date: 2019-09-21
Authors@R: c(
    person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")),
    person("Davis", "Vaughan", email = "dvaughan@business-science.io", role = c("aut"))
  )
Description: 
    Get the time series index (date or date-time component), 
    time series signature (feature extraction of date or date-time component for time series machine learning), 
    and time series summary (summary attributes about time series). 
    Create future time series based on properties of 
    existing time series index using logistic regression.  
    Coerce between time-based tibbles ('tbl') and 'xts', 'zoo', and 'ts'. 
    Methods discussed herein are commonplace in machine learning, and have been cited in various
    literature. Refer to "Calendar Effects" in papers such as 
    Taieb, Souhaib Ben. "Machine learning strategies for multi-step-ahead time series forecasting." 
    Universit Libre de Bruxelles, Belgium (2014): 75-86. <http://souhaib-bentaieb.com/pdf/2014_phd.pdf>.
URL: https://github.com/business-science/timetk
BugReports: https://github.com/business-science/timetk/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
Depends: R (>= 3.3.0)
Imports: dplyr (>= 0.7.0), lazyeval (>= 0.2.0), lubridate (>= 1.6.0),
        padr (>= 0.3.0), purrr (>= 0.2.2), readr (>= 1.0.0), stringi
        (>= 1.1.5), tibble (>= 1.2), tidyr (>= 0.6.1), xts (>= 0.9-7),
        zoo (>= 1.7-14)
Suggests: tidyverse, broom, forcats, forecast, knitr, rmarkdown,
        robets, scales, stringr, testthat, tidyquant, fracdiff,
        timeDate, timeSeries, tseries
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-09-25 10:53:22 UTC; mdancho
Author: Matt Dancho [aut, cre],
  Davis Vaughan [aut]
Maintainer: Matt Dancho <mdancho@business-science.io>
Repository: CRAN
Date/Publication: 2019-09-25 13:50:02 UTC
Built: R 3.5.1; ; 2020-02-18 20:46:08 UTC; windows
