{"id":13571228,"url":"https://github.com/business-science/modeltime","last_synced_at":"2025-04-08T02:42:34.958Z","repository":{"id":37483425,"uuid":"256504051","full_name":"business-science/modeltime","owner":"business-science","description":"Modeltime unlocks time series forecast models and machine learning in one framework","archived":false,"fork":false,"pushed_at":"2024-01-04T19:44:05.000Z","size":53364,"stargazers_count":499,"open_issues_count":55,"forks_count":81,"subscribers_count":28,"default_branch":"master","last_synced_at":"2024-04-23T19:24:36.557Z","etag":null,"topics":["arima","data-science","deep-learning","ets","forecasting","machine-learning","machine-learning-algorithms","modeltime","prophet","r-package","tbats","tidymodeling","tidymodels","time","time-series","time-series-analysis","timeseries","timeseries-forecasting"],"latest_commit_sha":null,"homepage":"https://business-science.github.io/modeltime/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/business-science.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-04-17T13:00:41.000Z","updated_at":"2024-06-08T00:44:18.509Z","dependencies_parsed_at":"2024-06-08T00:43:58.919Z","dependency_job_id":"7a4d7831-4c56-495f-aefc-a8b5ac64fe9b","html_url":"https://github.com/business-science/modeltime","commit_stats":{"total_commits":734,"total_committers":14,"mean_commits":52.42857142857143,"dds":"0.13760217983651224","last_synced_commit":"0b2a9156246a6bb6f22f0ea4e9937f0260106b9f"},"previous_names":[],"tags_count":16,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/business-science","download_url":"https://codeload.github.com/business-science/modeltime/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247767232,"owners_count":20992538,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["arima","data-science","deep-learning","ets","forecasting","machine-learning","machine-learning-algorithms","modeltime","prophet","r-package","tbats","tidymodeling","tidymodels","time","time-series","time-series-analysis","timeseries","timeseries-forecasting"],"created_at":"2024-08-01T14:01:00.026Z","updated_at":"2025-04-08T02:42:34.928Z","avatar_url":"https://github.com/business-science.png","language":"R","funding_links":[],"categories":["R","📦 Packages"],"sub_categories":["R"],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\",\n  message = F,\n  warning = F,\n  dpi = 200\n)\n```\n\n# modeltime\n\n\n\u003c!-- badges: start --\u003e\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime)](https://cran.r-project.org/package=modeltime)\n![](http://cranlogs.r-pkg.org/badges/modeltime?color=brightgreen)\n![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime?color=brightgreen)\n[![Codecov test coverage](https://codecov.io/gh/business-science/modeltime/branch/master/graph/badge.svg)]( https://app.codecov.io/gh/business-science/modeltime?branch=master)\n[![R-CMD-check](https://github.com/business-science/modeltime/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/modeltime/actions)\n\u003c!-- badges: end --\u003e\n\n\u003e Tidy time series forecasting in `R`. \n\nMission: Our number 1 goal is to make high-performance time series analysis easier, faster, and more scalable. Modeltime solves this with a simple to use infrastructure for modeling and forecasting time series. \n\n## Quickstart Video\n\nFor those that prefer video tutorials, we have an [11-minute YouTube Video](https://www.youtube.com/watch?v=-bCelif-ENY) that walks you through the Modeltime Workflow. \n\n\u003ca href=\"https://www.youtube.com/watch?v=-bCelif-ENY\" target=\"_blank\"\u003e\n\u003cp style='text-align:center;'\u003e\n\u003cimg src= \"vignettes/modeltime-video.jpg\"\nalt=\"Introduction to Modeltime\" width=\"60%\"/\u003e\n\u003c/p\u003e\n\u003cp style='text-align:center'\u003e(Click to Watch on YouTube)\u003c/p\u003e\n\u003c/a\u003e\n\n\n\n## Tutorials\n\n- [__Getting Started with Modeltime__](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html): A walkthrough of the 6-Step Process for using `modeltime` to forecast\n\n- [__Modeltime Documentation__](https://business-science.github.io/modeltime/): Learn how to __use__ `modeltime`, __find__ _Modeltime Models_, and __extend__ `modeltime` so you can use new algorithms inside the _Modeltime Workflow_. \n\n\n\n## Installation\n\nCRAN version:\n\n``` r\ninstall.packages(\"modeltime\", dependencies = TRUE)\n```\n\nDevelopment version:\n\n``` r\nremotes::install_github(\"business-science/modeltime\", dependencies = TRUE)\n```\n\n## Why modeltime?\n\n\n\u003e Modeltime unlocks time series models and machine learning in one framework \n\n```{r, echo=F, out.width='100%', fig.align='center'}\nknitr::include_graphics(\"vignettes/forecast_plot.jpg\")\n```\n\nNo need to switch back and forth between various frameworks. `modeltime` unlocks machine learning \u0026 classical time series analysis.\n\n  - __forecast__: Use ARIMA, ETS, and more models coming (`arima_reg()`, `arima_boost()`, \u0026 `exp_smoothing()`). \n  - __prophet__: Use Facebook's Prophet algorithm (`prophet_reg()` \u0026 `prophet_boost()`)\n  - __tidymodels__: Use any `parsnip` model: `rand_forest()`, `boost_tree()`, `linear_reg()`, `mars()`, `svm_rbf()` to forecast \n\n## Forecast faster\n\n\u003e A streamlined workflow for forecasting\n\nModeltime incorporates a [streamlined workflow (see Getting Started with Modeltime)](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html) for using best practices to forecast.\n\n\u003chr\u003e\n\n```{r, echo=F, out.width='100%', fig.align='center', fig.cap=\"A streamlined workflow for forecasting\"}\nknitr::include_graphics(\"vignettes/modeltime_workflow.jpg\")\n```\n\n\u003chr\u003e\n\n\n\n## Meet the modeltime ecosystem \n\n\u003e Learn a growing ecosystem of forecasting packages\n\n```{r, echo=F, out.width='100%', fig.align='center', fig.cap=\"The modeltime ecosystem is growing\"}\nknitr::include_graphics(\"man/figures/modeltime_ecosystem.jpg\")\n```\n\nModeltime is part of a __growing ecosystem__ of Modeltime forecasting packages. \n\n- [Modeltime (Machine Learning)](https://business-science.github.io/modeltime/)\n\n- [Modeltime H2O (AutoML)](https://business-science.github.io/modeltime.h2o/)\n\n- [Modeltime GluonTS (Deep Learning)](https://business-science.github.io/modeltime.gluonts/)\n\n- [Modeltime Ensemble (Blending Forecasts)](https://business-science.github.io/modeltime.ensemble/)\n\n- [Modeltime Resample (Backtesting)](https://business-science.github.io/modeltime.resample/)\n\n- [Timetk (Feature Engineering, Data Wrangling, Time Series Visualization)](https://business-science.github.io/timetk/)\n\n\n## Summary\n\nModeltime is an amazing ecosystem for time series forecasting. But it can take a long time to learn: \n\n- Many algorithms\n- Ensembling and Resampling\n- Machine Learning\n- Deep Learning\n- Scalable Modeling: 10,000+ time series\n\nYour probably thinking how am I ever going to learn time series forecasting. Here's the solution that will save you years of struggling. \n\n## Take the High-Performance Forecasting Course\n\n\u003e Become the forecasting expert for your organization\n\n\u003ca href=\"https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/\" target=\"_blank\"\u003e\u003cimg src=\"https://www.filepicker.io/api/file/bKyqVAi5Qi64sS05QYLk\" alt=\"High-Performance Time Series Forecasting Course\" width=\"100%\" style=\"box-shadow: 0 0 5px 2px rgba(0, 0, 0, .5);\"/\u003e\u003c/a\u003e\n\n[_High-Performance Time Series Course_](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting/)\n\n### Time Series is Changing\n\nTime series is changing. __Businesses now need 10,000+ time series forecasts every day.__ This is what I call a _High-Performance Time Series Forecasting System (HPTSF)_ - Accurate, Robust, and Scalable Forecasting. \n\n __High-Performance Forecasting Systems will save companies by improving accuracy and scalability.__ Imagine what will happen to your career if you can provide your organization a \"High-Performance Time Series Forecasting System\" (HPTSF System).\n\n### How to Learn High-Performance Time Series Forecasting\n\nI teach how to build a HPTFS System in my [__High-Performance Time Series Forecasting Course__](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting). You will learn:\n\n- __Time Series Machine Learning__ (cutting-edge) with `Modeltime` - 30+ Models (Prophet, ARIMA, XGBoost, Random Forest, \u0026 many more)\n- __Deep Learning__ with `GluonTS` (Competition Winners)\n- __Time Series Preprocessing__, Noise Reduction, \u0026 Anomaly Detection\n- __Feature engineering__ using lagged variables \u0026 external regressors\n- __Hyperparameter Tuning__\n- __Time series cross-validation__\n- __Ensembling__ Multiple Machine Learning \u0026 Univariate Modeling Techniques (Competition Winner)\n- __Scalable Forecasting__ - Forecast 1000+ time series in parallel\n- and more.\n\n\u003cp class=\"text-center\" style=\"font-size:24px;\"\u003e\nBecome the Time Series Expert for your organization.\n\u003c/p\u003e\n\u003cbr\u003e\n\u003cp class=\"text-center\" style=\"font-size:30px;\"\u003e\n\u003ca href=\"https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting\"\u003eTake the High-Performance Time Series Forecasting Course\u003c/a\u003e\n\u003c/p\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusiness-science%2Fmodeltime","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbusiness-science%2Fmodeltime","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusiness-science%2Fmodeltime/lists"}