{"id":21428286,"url":"https://github.com/mlverse/tft","last_synced_at":"2025-07-14T10:31:50.236Z","repository":{"id":37530272,"uuid":"328174947","full_name":"mlverse/tft","owner":"mlverse","description":"R implementation of Temporal Fusion Transformers","archived":false,"fork":false,"pushed_at":"2024-11-16T22:32:23.000Z","size":4605,"stargazers_count":28,"open_issues_count":14,"forks_count":9,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-07-07T13:40:40.768Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://mlverse.github.io/tft/","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/mlverse.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}},"created_at":"2021-01-09T14:36:32.000Z","updated_at":"2025-05-03T05:22:10.000Z","dependencies_parsed_at":"2023-01-17T14:46:27.513Z","dependency_job_id":null,"html_url":"https://github.com/mlverse/tft","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mlverse/tft","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlverse%2Ftft","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlverse%2Ftft/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlverse%2Ftft/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlverse%2Ftft/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mlverse","download_url":"https://codeload.github.com/mlverse/tft/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mlverse%2Ftft/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265280694,"owners_count":23739852,"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":[],"created_at":"2024-11-22T22:12:25.475Z","updated_at":"2025-07-14T10:31:45.221Z","avatar_url":"https://github.com/mlverse.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  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# tft\n\n\u003c!-- badges: start --\u003e\n[![R build status](https://github.com/mlverse/tft/workflows/R-CMD-check/badge.svg)](https://github.com/mlverse/tft/actions)\n[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)\n[![CRAN status](https://www.r-pkg.org/badges/version/tft)](https://CRAN.R-project.org/package=tft)\n[![](https://cranlogs.r-pkg.org/badges/tft)](https://cran.r-project.org/package=tft)\n[![Codecov test coverage](https://codecov.io/gh/mlverse/tft/branch/master/graph/badge.svg)](https://codecov.io/gh/mlverse/tft?branch=master)\n\n\u003c!-- badges: end --\u003e\n\n\u003e An R implementation of [tft: Temporal Fusion Transformer](https://arxiv.org/pdf/1912.09363.pdf).\n\nThe Temporal Fusion Transformer is a neural network architecture proposed by Bryan Lim et al. with the goal of making multi-horizon time series forecasts for\nmultiple time series in a single model.\n\nThe main difference between TFT and conventional forecasting methodologies is the\nway its architecture allows encoding different types of input data that can exist\nin forecasting problems. For instance, the model allows handling static covariates\nand time varying (known and unknown) differently. tft also showed [promising benchmarks](https://ai.googleblog.com/2021/12/interpretable-deep-learning-for-time.html).\n\nThe code in this repository is heavily inspired in code from [akeskiner/Temporal_Fusion_Transform](https://github.com/akeskiner/Temporal_Fusion_Transform), [jdb78/pytorch-forecasting](https://github.com/jdb78/pytorch-forecasting) and\nthe original implementation [here](https://github.com/google-research/google-research/tree/master/tft).\n\n## Installation\n\nYou can install the development version [GitHub](https://github.com/) with:\n\n```r\n# install.packages(\"remotes\")\nremotes::install_github(\"mlverse/tft\")\n```\n\nRead the [Getting Started](https://mlverse.github.io/tft/articles/Getting-started.html) guide to fit your first\nmodel with tft.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlverse%2Ftft","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmlverse%2Ftft","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmlverse%2Ftft/lists"}