{"id":20365952,"url":"https://github.com/business-science/modeltime.resample","last_synced_at":"2025-04-12T04:54:34.165Z","repository":{"id":53786714,"uuid":"304084354","full_name":"business-science/modeltime.resample","owner":"business-science","description":"Resampling Tools for Time Series Forecasting with Modeltime","archived":false,"fork":false,"pushed_at":"2024-01-04T20:18:42.000Z","size":18796,"stargazers_count":19,"open_issues_count":9,"forks_count":5,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-12T04:54:26.128Z","etag":null,"topics":["accuracy-metrics","backtesting","bootstrap","bootstrapping","cross-validation","forecasting","modeltime","modeltime-resample","r-package","resampling","statistics","tidymodels","time-series"],"latest_commit_sha":null,"homepage":"https://business-science.github.io/modeltime.resample/","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":"2020-10-14T17:13:41.000Z","updated_at":"2024-08-23T12:19:39.000Z","dependencies_parsed_at":"2023-12-13T12:58:57.137Z","dependency_job_id":null,"html_url":"https://github.com/business-science/modeltime.resample","commit_stats":{"total_commits":70,"total_committers":2,"mean_commits":35.0,"dds":"0.014285714285714235","last_synced_commit":"79ccd657e80f031ddb6a80233c9e85bd14862abf"},"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.resample","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.resample/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.resample/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/business-science%2Fmodeltime.resample/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/business-science","download_url":"https://codeload.github.com/business-science/modeltime.resample/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248519472,"owners_count":21117757,"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":["accuracy-metrics","backtesting","bootstrap","bootstrapping","cross-validation","forecasting","modeltime","modeltime-resample","r-package","resampling","statistics","tidymodels","time-series"],"created_at":"2024-11-15T00:21:28.778Z","updated_at":"2025-04-12T04:54:34.145Z","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\nlibrary(modeltime)\nlibrary(modeltime.resample)\n```\n\n# modeltime.resample \u003ca href=\"https://business-science.github.io/modeltime.resample/\"\u003e\u003cimg src=\"man/figures/logo.png\" align=\"right\" height=\"138\" alt=\"modeltime.resample website\" /\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n[![CRAN status](https://www.r-pkg.org/badges/version/modeltime.resample)](https://CRAN.R-project.org/package=modeltime.resample)\n![](http://cranlogs.r-pkg.org/badges/modeltime.resample?color=brightgreen)\n![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.resample?color=brightgreen)\n[![R-CMD-check](https://github.com/business-science/modeltime.resample/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/modeltime.resample/actions/workflows/R-CMD-check.yaml)\n[![Codecov test coverage](https://codecov.io/gh/business-science/modeltime.resample/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/modeltime.resample?branch=master)\n\u003c!-- badges: end --\u003e\n\n\u003e __Model Performance and Stability Assessment Tools__ for Single Time Series, Panel Data, \u0026 Cross-Sectional Time Series Analysis\n\n\nA `modeltime` extension that implements ___forecast resampling tools___ that assess __time-based model performance and stability__ for a single time series, panel data, and cross-sectional time series analysis.\n\n\n\n```{r, echo=F, out.width='100%'}\nknitr::include_graphics(\"man/figures/cross_validation_plan.jpg\")\n```\n\n## Installation\n\nCRAN version:\n\n``` r\ninstall.packages(\"modeltime.resample\")\n```\n\nDevelopment version (latest features):\n\n``` r\nremotes::install_github(\"business-science/modeltime.resample\")\n```\n\n## Why Modeltime Resample?\n\nResampling time series is an important strategy to __evaluate the stability of models over time.__ However, it's a pain to do this because it requires multiple for-loops to generate the predictions for multiple models and potentially multiple time series groups. __Modeltime Resample simplifies the iterative forecasting process taking the pain away.__\n\nModeltime Resample makes it easy to:\n\n1. __Iteratively generate predictions__ from time series cross-validation plans.\n2. __Evaluate the resample predictions__ to compare many time series models across multiple time-series windows.\n\nHere is an example from [_Resampling Panel Data_](https://business-science.github.io/modeltime.resample/articles/panel-data.html), where we can see that Prophet Boost and XGBoost Models outperform Prophet with Regressors for the Walmart Time Series Panel Dataset using the 6-Slice Time Series Cross Validation plan shown above. \n\n```{r, echo=F, out.width='100%', fig.cap=\"Model Accuracy for 6 Time Series Resamples\"}\nknitr::include_graphics(\"man/figures/plotly_resample_error_plot.jpg\")\n```\n\n```{r, echo=F, out.width='80%', fig.cap=\"Resampled Model Accuracy (3 Models, 6 Resamples, 7 Time Series Groups)\"}\nknitr::include_graphics(\"man/figures/gt_accuracy_table.jpg\")\n```\n\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. [Resampling a Single Time Series](https://business-science.github.io/modeltime.resample/articles/getting-started.html): Learn the basics of time series resample evaluation. \n3. [Resampling Panel Data](https://business-science.github.io/modeltime.resample/articles/panel-data.html): An advanced tutorial on resample evaluation with __multiple time series groups (Panel Data)__\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.resample","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbusiness-science%2Fmodeltime.resample","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbusiness-science%2Fmodeltime.resample/lists"}