{"id":20365973,"url":"https://github.com/business-science/modeltime.gluonts","last_synced_at":"2025-06-15T00:33:14.626Z","repository":{"id":54250583,"uuid":"313022971","full_name":"business-science/modeltime.gluonts","owner":"business-science","description":"GluonTS Deep Learning with Modeltime","archived":false,"fork":false,"pushed_at":"2024-01-04T22:31:45.000Z","size":14209,"stargazers_count":41,"open_issues_count":34,"forks_count":9,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-12T04:54:38.911Z","etag":null,"topics":["deep-learning","forecasting","gluonts","gluonts-deep-learning","modeltime","r-package","tidymodels","time-series"],"latest_commit_sha":null,"homepage":"https://business-science.github.io/modeltime.gluonts/","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}},"created_at":"2020-11-15T12:09:32.000Z","updated_at":"2025-03-07T21:06:53.000Z","dependencies_parsed_at":"2024-01-13T07:25:25.020Z","dependency_job_id":"585e7988-03ea-45fc-8811-895555068a8d","html_url":"https://github.com/business-science/modeltime.gluonts","commit_stats":{"total_commits":210,"total_committers":2,"mean_commits":105.0,"dds":0.03809523809523807,"last_synced_commit":"6a90370393bc527ee6b68e5368e9d7264d493e19"},"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/business-science/modeltime.gluonts","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.gluonts","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.gluonts/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.gluonts/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.gluonts/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/business-science","download_url":"https://codeload.github.com/business-science/modeltime.gluonts/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.gluonts/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259905344,"owners_count":22929915,"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":["deep-learning","forecasting","gluonts","gluonts-deep-learning","modeltime","r-package","tidymodels","time-series"],"created_at":"2024-11-15T00:21:33.279Z","updated_at":"2025-06-15T00:33:14.597Z","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  message = F, \n  warning = F, \n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"90%\",\n  fig.align = \"center\"\n)\n```\n\n# modeltime.gluonts \u003ca href=\"https://business-science.github.io/modeltime.gluonts/\"\u003e\u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"138\" alt=\"modeltime.gluonts website\" /\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/modeltime.gluonts)](https://cran.r-project.org/package=modeltime)\n![](http://cranlogs.r-pkg.org/badges/modeltime.gluonts?color=brightgreen)\n![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.gluonts?color=brightgreen)\n[![Codecov test coverage](https://codecov.io/gh/business-science/modeltime.gluonts/branch/master/graph/badge.svg)](https://codecov.io/gh/business-science/modeltime.gluonts?branch=master)\n[![R-CMD-check](https://github.com/business-science/modeltime.gluonts/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/modeltime.gluonts/actions)\n\u003c!-- badges: end --\u003e\n\n\u003e GluonTS Deep Learning in R. \n\n\n## GluonTS Deep Learning in R\n\nModeltime GluonTS integrates the __Python GluonTS Deep Learning Library__, making it easy to develop forecasts using Deep Learning for those that are comfortable with the [Modeltime Forecasting Workflow](https://business-science.github.io/modeltime/). \n\n```{r, echo=F}\nknitr::include_graphics(\"man/figures/m4_hourly_forecast.jpg\")\n```\n\n\n## Installation Requirements\n\n__Important: This package is being maintained on GitHub (not CRAN). Please install the GitHub version, which is updated with the latest features:__\n\n```\n# Install GitHub Version \nremotes::install_github(\"business-science/modeltime.gluonts\")\n\n# Install Python Dependencies\nmodeltime.gluonts::install_gluonts()\n```\n\nFor more detailed installation instructions and troubleshooting guidance, visit our [Installation Guide](https://business-science.github.io/modeltime.gluonts/articles/managing-envs.html). \n\n## Make Your First DeepAR Model\n\nMake your first `deep_ar()` model, which connects to the GluonTS `DeepAREstimator()`. For a more detailed walkthough, visit our [Getting Started Guide](https://business-science.github.io/modeltime.gluonts/articles/getting-started.html).\n\n```{r example, eval = F}\nlibrary(modeltime.gluonts)\nlibrary(tidymodels)\nlibrary(tidyverse)\n\n# Fit a GluonTS DeepAR Model\nmodel_fit_deepar \u003c- deep_ar(\n    id                    = \"id\",\n    freq                  = \"M\",\n    prediction_length     = 24,\n    lookback_length       = 48,\n    epochs                = 5\n) %\u003e%\n    set_engine(\"gluonts_deepar\") %\u003e%\n    fit(value ~ ., training(m750_splits))\n\n# Forecast with 95% Confidence Interval\nmodeltime_table(\n    model_fit_deepar\n) %\u003e%\n    modeltime_calibrate(new_data = testing(m750_splits)) %\u003e%\n    modeltime_forecast(\n        new_data      = testing(m750_splits),\n        actual_data   = m750,\n        conf_interval = 0.95\n    ) %\u003e%\n    plot_modeltime_forecast(.interactive = FALSE)\n```\n\n```{r, echo = F}\nknitr::include_graphics(\"man/figures/deepar_example_1.png\")\n```\n\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\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusiness-science%2Fmodeltime.gluonts","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbusiness-science%2Fmodeltime.gluonts","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusiness-science%2Fmodeltime.gluonts/lists"}