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: |
1.2.2 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
StanHeaders, timetk (≥ 2.8.1), parsnip (≥ 0.2.1), dials, yardstick (≥ 0.0.8), workflows (≥ 0.1.3), hardhat (≥ 1.0.0), rlang (≥ 0.1.2), glue, plotly, reactable, gt, ggplot2, tibble, tidyr, dplyr, purrr, stringr, forcats, scales, janitor, parallel, parallelly, doParallel, foreach, magrittr, forecast, xgboost (≥ 1.2.0.1), prophet, methods, cli |
| Suggests: |
rstan, slider, sparklyr, tidymodels, workflowsets, recipes, rsample, tune (≥ 0.2.0), tidyverse, lubridate, progress, testthat, roxygen2, kernlab, glmnet, thief, smooth, greybox, earth, randomForest, tidyquant, trelliscopejs, knitr, rmarkdown (≥ 2.9), webshot, qpdf, covr, TSrepr |
| Published: |
2022-06-07 |
| 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 |