The time series forecasting framework for use with the 'tidymodels' ecosystem. Models include ARIMA, Exponential Smoothing, and additional time series models from the 'forecast' and 'prophet' packages. Refer to "Forecasting Principles & Practice, Second edition" (<https://otexts.com/fpp2/>). Refer to "Prophet: forecasting at scale" (<https://research.facebook.com/blog/2017/02/prophet-forecasting-at-scale/>.).
Version: 1.3.1 Depends: R (≥ 3.5.0) Imports: StanHeaders, timetk (≥ 2.8.1), parsnip (≥ 0.2.1), dials, yardstick (≥ 0.0.8), workflows (≥ 1.0.0), hardhat (≥ 1.0.0), rlang (≥ 0.1.2), glue, plotly, reactable, gt, ggplot2, tibble, tidyr, dplyr (≥ 1.1.0), purrr, stringr, forcats, scales, janitor, parallel, parallelly, doParallel, foreach, magrittr, forecast, xgboost (≥ 1.2.0.1), prophet, methods, cli, tidymodels Suggests: rstan, slider, sparklyr, workflowsets, recipes, rsample, tune (≥ 0.2.0), lubridate, testthat, kernlab, glmnet, thief, smooth, greybox, earth, randomForest, trelliscopejs, knitr, rmarkdown (≥ 2.9), webshot, qpdf, TSrepr Published: 2024-10-22 DOI: 10.32614/CRAN.package.modeltime Author: Matt Dancho [aut, cre], Business Science [cph] Maintainer: Matt Dancho <mdancho at business-science.io> BugReports: https://github.com/business-science/modeltime/issues License: MIT + file LICENSE URL: https://github.com/business-science/modeltime, https://business-science.github.io/modeltime/ NeedsCompilation: no Materials: README NEWS In views: TimeSeries CRAN checks: modeltime results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=modeltime to link to this page.
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