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: | 2.2.0 |
| 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 |
| Suggests: | tidyquant, tidymodels, modeltime, workflows, parsnip, tune, yardstick, tidyverse, knitr, rmarkdown, robets, broom, scales, testthat, fracdiff, timeSeries, tseries, roxygen2 |
| Published: | 2020-07-18 |
| 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 Plotting_Seasonality_and_Correlation |
| Package source: | timetk_2.2.0.tar.gz |
| Windows binaries: | r-devel: timetk_2.2.0.zip, r-release: timetk_2.2.0.zip, r-oldrel: timetk_2.2.0.zip |
| macOS binaries: | r-release: timetk_2.2.0.tgz, r-oldrel: timetk_2.2.0.tgz |
| Old sources: | timetk archive |
| Reverse imports: | alphavantager, anomalize, modeltime, PortalHacienda, RTL, sweep, tidyquant |
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