timetk: A Tool Kit for Working with Time Series

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>.

Version: 0.1.2
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
Published: 2019-09-25
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: Time Series Coercion Using timetk
Working with the Time Series Index using timetk
Making a Future Time Series Index using timetk
Forecasting Using a Time Series Signature with timetk
Package source: timetk_0.1.2.tar.gz
Windows binaries: r-devel: timetk_0.1.2.zip, r-release: timetk_0.1.2.zip, r-oldrel: timetk_0.1.2.zip
OS X binaries: r-release: timetk_0.1.2.tgz, r-oldrel: timetk_0.1.2.tgz
Old sources: timetk archive

Reverse dependencies:

Reverse imports: alphavantager, anomalize, sweep, tidyquant


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