A toolkit for working with time series in R
Making time series analysis in R easier.
Mission: To make time series analysis in R easier, faster, and more
enjoyable.
Download the development version with latest features:
remotes::install_github("business-science/timetk")
Or, download CRAN approved version:
install.packages("timetk")
There are many R packages for working with Time Series data. Here’s
how timetk compares to the “tidy” time series R packages for data
visualization, wrangling, and feature engineeering (those that leverage
data frames or tibbles).
Full Time Series Machine Learning and Feature Engineering
Tutorial
API Documentation for
articles and a complete list of function
references.
Timetk is an amazing package that is part of the modeltime ecosystem
for time series analysis and forecasting. The forecasting system is
extensive, and it can take a long time to learn:
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The timetk package wouldn’t be possible without other amazing time
series packages.
timetk function that uses a period (frequency) argument owes it tots().plot_acf_diagnostics(): Leverages stats::acf(), stats::pacf()stats::ccf()plot_stl_diagnostics(): Leverages stats::stl()timetk makes heavyfloor_date(), ceiling_date(), and duration() for%+time% & %-time%):"2012-01-01" %+time% "1 month 4 days" uses lubridate tots, and itstidyverts (fable, tsibble, feasts, andfabletools).ts_impute_vec() function for low-level vectorized imputationna.interp() under the hood.ts_clean_vec() function for low-level vectorized imputationtsclean() under the hood.auto_lambda() uses BoxCox.Lambda(). timetk does not import tibbletime, it uses much of the innovativetk_make_timeseries() - Extends seq.Date() and seq.POSIXt()filter_by_time(), between_time() - Uses innovative endpointslidify() is basically rollify() using slider (see below).purrr-syntax for complex rolling (sliding) calculations.slidify() uses slider::pslide under the hood.slidify_vec() uses slider::slide_vec() for simple vectorizedpad_by_time() function is a wrapper for padr::pad().step_ts_pad() to apply padding as a preprocessing recipe!ts system, which is the same system the forecast R package uses. ATSstudio.