{"id":13400625,"url":"https://github.com/tidyverts/feasts","last_synced_at":"2026-02-21T00:02:40.650Z","repository":{"id":45129073,"uuid":"135676255","full_name":"tidyverts/feasts","owner":"tidyverts","description":"Feature Extraction And Statistics for Time Series","archived":false,"fork":false,"pushed_at":"2026-02-09T00:37:45.000Z","size":32018,"stargazers_count":308,"open_issues_count":25,"forks_count":29,"subscribers_count":15,"default_branch":"main","last_synced_at":"2026-02-09T05:10:49.560Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://feasts.tidyverts.org/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tidyverts.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","contributing":null,"funding":null,"license":null,"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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2018-06-01T06:23:07.000Z","updated_at":"2026-02-09T00:35:07.000Z","dependencies_parsed_at":"2022-09-22T17:31:29.205Z","dependency_job_id":"39512167-7557-47ba-8f64-1302cefcd1be","html_url":"https://github.com/tidyverts/feasts","commit_stats":{"total_commits":597,"total_committers":9,"mean_commits":66.33333333333333,"dds":"0.15577889447236182","last_synced_commit":"1ad4cfd6df3868566d79932dffc3cd337029f766"},"previous_names":[],"tags_count":19,"template":false,"template_full_name":null,"purl":"pkg:github/tidyverts/feasts","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tidyverts%2Ffeasts","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tidyverts%2Ffeasts/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tidyverts%2Ffeasts/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tidyverts%2Ffeasts/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tidyverts","download_url":"https://codeload.github.com/tidyverts/feasts/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tidyverts%2Ffeasts/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29668637,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-20T23:24:07.480Z","status":"ssl_error","status_checked_at":"2026-02-20T23:24:06.202Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":[],"created_at":"2024-07-30T19:00:54.035Z","updated_at":"2026-02-21T00:02:40.636Z","avatar_url":"https://github.com/tidyverts.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 setup, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\noptions(width = 100)\n```\n\n# feasts \u003ca href='https://feasts.tidyverts.org'\u003e\u003cimg src='man/figures/logo.png' align=\"right\" height=\"138.5\" /\u003e\u003c/a\u003e\n\n\u003c!-- badges: start --\u003e\n[![R build status](https://github.com/tidyverts/feasts/workflows/R-CMD-check/badge.svg)](https://github.com/tidyverts/feasts/actions?workflow=R-CMD-check)\n[![Coverage status](https://codecov.io/gh/tidyverts/feasts/branch/master/graph/badge.svg)](https://app.codecov.io/gh/tidyverts/feasts?branch=master)\n[![CRAN status](https://www.r-pkg.org/badges/version/feasts)](https://CRAN.R-project.org/package=feasts)\n[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)\n\u003c!-- badges: end --\u003e\n\n## Overview\n\nfeasts provides a collection of tools for the analysis of time series data. The package name is an acronym comprising of its key features: *Feature Extraction And Statistics for Time Series*.\n\nThe package works with tidy temporal data provided by the [tsibble](https://github.com/tidyverts/tsibble) package to produce time series features, decompositions, statistical summaries and convenient visualisations. These features are useful in understanding the behaviour of time series data, and closely integrates with the tidy forecasting workflow used in the [fable](https://github.com/tidyverts/fable) package.\n\n## Installation\n\nYou could install the **stable** version from [CRAN](https://cran.r-project.org/package=feasts):\n\n```{r, eval = FALSE}\ninstall.packages(\"feasts\")\n```\n\nYou can install the **development** version from\n[GitHub](https://github.com/tidyverts/feasts) with:\n\n```{r gh-installation, eval = FALSE}\n# install.packages(\"remotes\")\nremotes::install_github(\"tidyverts/feasts\")\n```\n\n## Usage\n\n```{r pkgs, message = FALSE, warning = FALSE}\nlibrary(feasts)\nlibrary(tsibble)\nlibrary(tsibbledata)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(lubridate)\n```\n\n### Graphics\n\nVisualisation is often the first step in understanding the patterns in time series data. The package uses [ggplot2](https://github.com/tidyverse/ggplot2) to produce customisable graphics to visualise time series patterns.\n\n```{r graphics, fig.height=3}\naus_production %\u003e% gg_season(Beer)\naus_production %\u003e% gg_subseries(Beer)\naus_production %\u003e% filter(year(Quarter) \u003e 1991) %\u003e% gg_lag(Beer)\naus_production %\u003e% ACF(Beer) %\u003e% autoplot()\n```\n\n### Decompositions\n\nA common task in time series analysis is decomposing a time series into some simpler components. The feasts package supports two common time series decomposition methods:\n\n* Classical decomposition\n* STL decomposition\n\n\u003c!--\n* X11 decomposition\n* X-13ARIMA-SEATS decomposition\n--\u003e\n\n```{r dcmp}\ndcmp \u003c- aus_production %\u003e%\n  model(STL(Beer ~ season(window = Inf)))\ncomponents(dcmp)\n```\n```{r dcmp-plot}\ncomponents(dcmp) %\u003e% autoplot()\n```\n\n### Feature extraction and statistics\n\nExtract features and statistics across a large collection of time series to identify unusual/extreme time series, or find clusters of similar behaviour.\n\n```{r features}\naus_retail %\u003e%\n  features(Turnover, feat_stl)\n```\n\nThis allows you to visualise the behaviour of many time series (where the plotting methods above would show too much information).\n\n```{r features-plot}\naus_retail %\u003e%\n  features(Turnover, feat_stl) %\u003e%\n  ggplot(aes(x = trend_strength, y = seasonal_strength_year)) +\n  geom_point() +\n  facet_wrap(vars(State))\n```\n\nMost of Australian's retail industries are highly trended and seasonal for all states.\n\nIt's also easy to extract the most (and least) seasonal time series.\n\n```{r extreme}\nextreme_seasonalities \u003c- aus_retail %\u003e%\n  features(Turnover, feat_stl) %\u003e%\n  filter(seasonal_strength_year %in% range(seasonal_strength_year))\naus_retail %\u003e%\n  right_join(extreme_seasonalities, by = c(\"State\", \"Industry\")) %\u003e%\n  ggplot(aes(x = Month, y = Turnover)) +\n  geom_line() +\n  facet_grid(vars(State, Industry, scales::percent(seasonal_strength_year)),\n             scales = \"free_y\")\n```\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftidyverts%2Ffeasts","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftidyverts%2Ffeasts","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftidyverts%2Ffeasts/lists"}