timetk: A Tool Kit for Working with Time Series in R

Easy visualization, wrangling, and feature engineering of time series data for forecasting and machine learning prediction. 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>.

Version: 2.4.0
Depends: R (≥ 3.3.0)
Imports: recipes (≥ 0.1.4), rsample, dplyr (≥ 1.0.0), ggplot2, forcats, stringr, plotly, lazyeval (≥ 0.2.0), lubridate (≥ 1.6.0), padr (≥ 0.5.2), purrr (≥ 0.2.2), readr (≥ 1.3.0), stringi (≥ 1.4.6), tibble (≥ 3.0.3), tidyr (≥ 1.1.0), xts (≥ 0.9-7), zoo (≥ 1.7-14), rlang (≥ 0.4.7), tidyselect (≥ 1.1.0), slider, anytime, timeDate, forecast, hms, assertthat, generics
Suggests: tidyquant, tidymodels, modeltime, workflows, parsnip, tune, yardstick, tidyverse, knitr, rmarkdown, robets, broom, scales, testthat, fracdiff, timeSeries, tseries, roxygen2
Published: 2020-10-08
Author: Matt Dancho [aut, cre], Davis Vaughan [aut]
Maintainer: Matt Dancho <mdancho at business-science.io>
BugReports: https://github.com/business-science/timetk/issues
License: GPL (≥ 3)
URL: https://github.com/business-science/timetk
NeedsCompilation: no
Materials: README NEWS
In views: TimeSeries
CRAN checks: timetk results


Reference manual: timetk.pdf
Vignettes: Plotting Time Series
Package source: timetk_2.4.0.tar.gz
Windows binaries: r-devel: timetk_2.4.0.zip, r-release: timetk_2.3.0.zip, r-oldrel: timetk_2.3.0.zip
macOS binaries: r-release: timetk_2.4.0.tgz, r-oldrel: timetk_2.4.0.tgz
Old sources: timetk archive

Reverse dependencies:

Reverse imports: alphavantager, anomalize, modeltime, modeltime.ensemble, RTL, sweep, tidyquant
Reverse suggests: JFE


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