{"id":20046608,"url":"https://github.com/ncsoft/timesuperin","last_synced_at":"2025-08-20T06:11:35.895Z","repository":{"id":152713290,"uuid":"141547076","full_name":"ncsoft/timesuperin","owner":"ncsoft","description":"R library for time series data modeling","archived":false,"fork":false,"pushed_at":"2018-12-06T06:22:03.000Z","size":45,"stargazers_count":2,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-02T07:49:29.770Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/ncsoft.png","metadata":{"files":{"readme":"README-ENGLISH.md","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}},"created_at":"2018-07-19T08:17:30.000Z","updated_at":"2019-07-27T07:27:47.000Z","dependencies_parsed_at":null,"dependency_job_id":"976a226a-d029-4ff3-b3d2-ba6647f78ce3","html_url":"https://github.com/ncsoft/timesuperin","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ncsoft/timesuperin","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncsoft%2Ftimesuperin","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncsoft%2Ftimesuperin/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncsoft%2Ftimesuperin/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncsoft%2Ftimesuperin/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ncsoft","download_url":"https://codeload.github.com/ncsoft/timesuperin/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncsoft%2Ftimesuperin/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270110061,"owners_count":24528970,"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","status":"online","status_checked_at":"2025-08-12T02:00:09.011Z","response_time":80,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":"2024-11-13T11:25:00.557Z","updated_at":"2025-08-12T18:13:33.958Z","avatar_url":"https://github.com/ncsoft.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Timesuperin\n\nTimesuperin is R library for analyzing time-series data and detecting anomalies in them. The name Timesuperin is the abbreviation of “Time Superintendent”. The package generates a proper regression model based on time-series specificity and unique events in your data, and detect anomalies by checking any significant difference between estimated values and real ones.\n\n# Installation \n\nTo use the package, following libraries should be installed: \n* Rcpp\n* rstan \n* BH\n* stringi \n\nWhen these libraries are installed, execute following code. \n\n```{r}\ndevtools::install_github(\"ncsoft/timesuperin\")\n```\n\nIf R version higher than 3.5.0 is used, devtools may break installation. In this case, following code would be helpful. \n\n```{r}\nlibrary(devtools)\nfind_rtools() # If error is printed messaging R 3.5.0 compatibility, run following code \nassignInNamespace(\"version_info\", \n                 c(devtools:::version_info,\n                  list(\"3.5\" = list(version_min = \"3.3.0\", version_max = \"99.99.99\", path = \"bin\"))), \"devtools\")\n```\n\n\n## Mini example\n\nIn this example, we use a sample data in the package. Load train and test data in `timesuperin/resources`.\n\n```{r}\ntrain_data \u003c- read.csv('./train_data.csv')\ntest_data \u003c- read.csv('./test_data.csv')\n```\n\nTwo datasets are time-series data affected by two event variables. Let's plot training data. \n\n![](https://raw.githubusercontent.com/ncsoft/timesuperin/master/resources/train_data.png)\n\nNow we generate baseline time-series model as follows: \n\n```{r}\nmodel \u003c- model.timesuperin(train_data, model.type = 'lm', period = 6)\n```\n\nBased on the model generated, we try to detect anomalies in test data. \n\n```{r}\nanomaly.detect \u003c- detect_anomal.timesuperin(model, test_data, value = test_data$value)\n```\nThe result of anomaly detection is also provided by interval plot. \n\n```{r}\nanomaly.detect$Interval_Plot\n```\n\n![](https://raw.githubusercontent.com/ncsoft/timesuperin/master/resources/anomaly_detect.png)\n\n\nTwo points in data are out of bounds according to upr and lwr, which may be labelled as anomaly. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fncsoft%2Ftimesuperin","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fncsoft%2Ftimesuperin","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fncsoft%2Ftimesuperin/lists"}