{"id":32209748,"url":"https://github.com/gmgeorg/foreca","last_synced_at":"2026-02-21T18:04:52.118Z","repository":{"id":56936222,"uuid":"270420222","full_name":"gmgeorg/ForeCA","owner":"gmgeorg","description":"ForeCA: Forecastable Component Analysis in R","archived":false,"fork":false,"pushed_at":"2020-07-03T02:41:35.000Z","size":2136,"stargazers_count":16,"open_issues_count":0,"forks_count":5,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-12-09T20:37:28.510Z","etag":null,"topics":["blind-source-separation","dimensionality-reduction","forecasting","multivariate-timeseries","signal-processing","spectrum","time-series","time-series-analysis"],"latest_commit_sha":null,"homepage":"","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/gmgeorg.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-06-07T20:11:47.000Z","updated_at":"2025-11-06T09:07:41.000Z","dependencies_parsed_at":"2022-08-21T06:21:02.017Z","dependency_job_id":null,"html_url":"https://github.com/gmgeorg/ForeCA","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/gmgeorg/ForeCA","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gmgeorg%2FForeCA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gmgeorg%2FForeCA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gmgeorg%2FForeCA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gmgeorg%2FForeCA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gmgeorg","download_url":"https://codeload.github.com/gmgeorg/ForeCA/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gmgeorg%2FForeCA/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29689644,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-21T15:51:39.154Z","status":"ssl_error","status_checked_at":"2026-02-21T15:49:03.425Z","response_time":107,"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":["blind-source-separation","dimensionality-reduction","forecasting","multivariate-timeseries","signal-processing","spectrum","time-series","time-series-analysis"],"created_at":"2025-10-22T06:15:54.695Z","updated_at":"2026-02-21T18:04:52.113Z","avatar_url":"https://github.com/gmgeorg.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ForeCA R package\n\n[![](https://cranlogs.r-pkg.org/badges/ForeCA)](https://cran.r-project.org/package=ForeCA)\n\n\n**ForeCA** implements *Forecastable component analysis* in R.  For details on\nalgorithm \u0026 methodology see [*Forecastable Component Analysis*, JMLR, Goerg\n(2013)](http://proceedings.mlr.press/v28/goerg13.pdf).\n\n\n**In a nutshell:** *ForeCA* finds linear combinations of multivariate time\nseries that are most forecastable, where forecastability is measured by the\nspectral entropy of the resulting signal (linear combination of input).\n\n\n## Installation\n\nYou can install the stable version from\n[CRAN](https://cran.r-project.org/package=ForeCA):\n\n```r\ninstall.packages('ForeCA')\n```\n\nAlternatively, you can also install the latest version of **ForeCA** package\ndirectly from github as\n```{r}\nlibrary(devtools)\ndevtools::install_github(\"gmgeorg/ForeCA\")\n```\n\n## Usage\n\nThe workhorse function is `ForeCA::foreca()` which works just like the built-in\n`princomp` function for PCA. \n\n```{r}\nlibrary(ForeCA)\ncitation(\"ForeCA\")\n```\n\nFor a tutorial on how to use `foreca()` and the entire **ForeCA** suite of\nfunctions see the [introductory\nvignette](https://CRAN.R-project.org/package=ForeCA/vignettes/Introduction.html)\non CRAN. \n\n## References\n\n* **ForeCA references \u0026 applications in the literature** (non-exhaustive; see here for [full list of ForeCA citations](https://scholar.google.com/scholar?client=ubuntu\u0026channel=fs\u0026oe=utf-8\u0026um=1\u0026ie=UTF-8\u0026lr\u0026cites=5674198772479433271))\n\n  * Very interesting application of ForeCA to historical time series data of\n    temperature/climate to extract predictable climate signals. [Fischer, Matt.\n    (2016). *Predictable components in global speleothem δ18O*. Quaternary\n    Science Reviews. 131. 380-392.\n    10.1016/j.quascirev.2015.03.024.](https://www.researchgate.net/publication/275953571_Predictable_components_in_global_speleothem_d18O)\n  * ForeCA's forecastability measure, spectral entropy of a time series, can be\n    useful as a feature to characterize/visualize/predict performance of\n    different algorithms applied to a set of time series. [Kang, Yanfei \u0026\n    Hyndman, Rob \u0026 Smith-Miles, Kate. (2017). *Visualising forecasting algorithm\n    performance using time series instance spaces*. International Journal of\n    Forecasting. 33. 345-358.\n    10.1016/j.ijforecast.2016.09.004.](https://isidl.com/wp-content/uploads/2017/06/E3999-ISIDL.pdf)\n\n\n* **Cross-validated \u0026 SO posts** (non-exhaustive)\n\n    * [How to determine forecastability of time\n      series](https://stats.stackexchange.com/questions/126829/how-to-determine-forecastability-of-time-series)\n\n\n * **Blog posts** (by others)\n\n   * [Stock Forecasting with Machine Learning - Are Stock Prices\n     Predictable?](http://www.anlytcs.com/2016/04/stock-forecasting-with-machine-learning.html)\n     (2016/04/20)\n   * [Are stocks predictable?](http://fastml.com/are-stocks-predictable/)\n     (2014/02/20)\n   \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgmgeorg%2Fforeca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgmgeorg%2Fforeca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgmgeorg%2Fforeca/lists"}