{"id":20365947,"url":"https://github.com/business-science/modeltime.ensemble","last_synced_at":"2025-04-07T12:03:59.909Z","repository":{"id":38301889,"uuid":"297196584","full_name":"business-science/modeltime.ensemble","owner":"business-science","description":"Time Series Ensemble Forecasting","archived":false,"fork":false,"pushed_at":"2024-07-18T20:02:29.000Z","size":22245,"stargazers_count":78,"open_issues_count":11,"forks_count":18,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-31T11:04:06.186Z","etag":null,"topics":["ensemble","ensemble-learning","forecast","forecasting","modeltime","r-package","stacking","stacking-ensemble","tidymodels","time","time-series","timeseries"],"latest_commit_sha":null,"homepage":"https://business-science.github.io/modeltime.ensemble/","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":"NEWS.md","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":"2020-09-21T01:21:00.000Z","updated_at":"2025-03-17T20:03:54.000Z","dependencies_parsed_at":"2022-08-21T06:50:46.656Z","dependency_job_id":"2c8e5c94-2003-47ba-891a-64f3a1b39a7b","html_url":"https://github.com/business-science/modeltime.ensemble","commit_stats":{"total_commits":200,"total_committers":6,"mean_commits":"33.333333333333336","dds":0.09499999999999997,"last_synced_commit":"37f80be857188c428b651453b99c0d4afd8b6273"},"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.ensemble","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.ensemble/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.ensemble/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.ensemble/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/business-science","download_url":"https://codeload.github.com/business-science/modeltime.ensemble/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247648976,"owners_count":20972945,"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":["ensemble","ensemble-learning","forecast","forecasting","modeltime","r-package","stacking","stacking-ensemble","tidymodels","time","time-series","timeseries"],"created_at":"2024-11-15T00:21:26.404Z","updated_at":"2025-04-07T12:03:59.884Z","avatar_url":"https://github.com/business-science.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"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  message = F,\n  warning = F,\n  paged.print = FALSE,\n  fig.path = \"man/figures/README-\",\n  # out.width = \"100%\"\n  fig.align = 'center'\n)\n```\n\n# modeltime.ensemble \u003ca href=\"https://business-science.github.io/modeltime.ensemble/\"\u003e\u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"138\" alt=\"modeltime.ensemble website\" /\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime.ensemble)](https://cran.r-project.org/package=modeltime.ensemble)\n![](http://cranlogs.r-pkg.org/badges/modeltime.ensemble?color=brightgreen)\n![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.ensemble?color=brightgreen)\n[![R-CMD-check](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/modeltime.ensemble/actions/workflows/R-CMD-check.yaml)\n[![Codecov test coverage](https://codecov.io/gh/business-science/modeltime.ensemble/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/modeltime.ensemble?branch=master)\n\n\u003c!-- badges: end --\u003e\n\n\n\u003e Ensemble Algorithms for Time Series Forecasting with Modeltime\n\nA `modeltime` extension that implements ___ensemble forecasting methods___ including model averaging, weighted averaging, and stacking.\n\n```{r, echo=F, out.width='100%', fig.align='center'}\nknitr::include_graphics(\"vignettes/stacking.jpg\")\n```\n\n## Installation\n\nInstall the CRAN version:\n\n``` r\ninstall.packages(\"modeltime.ensemble\")\n```\n\nOr, install the development version:\n\n``` r\nremotes::install_github(\"business-science/modeltime.ensemble\")\n```\n\n## Getting Started\n\n1. [Getting Started with Modeltime](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html): Learn the basics of forecasting with Modeltime. \n2. [Getting Started with Modeltime Ensemble](https://business-science.github.io/modeltime.ensemble/articles/getting-started-with-modeltime-ensemble.html): Learn the basics of forecasting with Modeltime ensemble models. \n\n\n## Make Your First Ensemble in Minutes\n\nLoad the following libraries.\n\n```{r}\nlibrary(tidymodels)\nlibrary(modeltime)\nlibrary(modeltime.ensemble)\nlibrary(dplyr)\nlibrary(timetk)\n```\n\n#### Step 1 - Create a Modeltime Table\n\nCreate a _Modeltime Table_ using the `modeltime` package. \n\n```{r}\nm750_models\n```\n\n#### Step 2 - Make a Modeltime Ensemble\n\nThen turn that Modeltime Table into a ___Modeltime Ensemble.___\n\n```{r}\nensemble_fit \u003c- m750_models %\u003e%\n    ensemble_average(type = \"mean\")\n\nensemble_fit\n```\n\n#### Step 3 - Forecast!\n\nTo forecast, just follow the [Modeltime Workflow](https://business-science.github.io/modeltime/articles/getting-started-with-modeltime.html). \n\n```{r}\n# Calibration\ncalibration_tbl \u003c- modeltime_table(\n    ensemble_fit\n) %\u003e%\n    modeltime_calibrate(testing(m750_splits), quiet = FALSE)\n\n# Forecast vs Test Set\ncalibration_tbl %\u003e%\n    modeltime_forecast(\n        new_data    = testing(m750_splits),\n        actual_data = m750\n    ) %\u003e%\n    plot_modeltime_forecast(.interactive = FALSE)\n```\n\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## 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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusiness-science%2Fmodeltime.ensemble","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbusiness-science%2Fmodeltime.ensemble","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusiness-science%2Fmodeltime.ensemble/lists"}