{ "_id": "6ad755200d12c08e591b8f88", "Package": "oddstream", "Type": "Package", "Title": "Outlier Detection in Data Streams", "Version": "0.5.1", "Maintainer": "Priyanga Dilini Talagala ", "Authors@R": "c(\nperson(\"Priyanga Dilini\", \"Talagala\", email=\"pritalagala@gmail.com\", role= c(\"aut\",\"cre\")),\nperson(\"Rob J.\", \"Hyndman\", role=c(\"ths\")),\nperson(\"Kate\", \"Smith-Miles\", role=c(\"ths\"))\n)", "Description": "We proposes a framework that provides real time support\nfor early detection of anomalous series within a large\ncollection of streaming time series data. By definition,\nanomalies are rare in comparison to a system's typical\nbehaviour. We define an anomaly as an observation that is very\nunlikely given the forecast distribution. The algorithm first\nforecasts a boundary for the system's typical behaviour using a\nrepresentative sample of the typical behaviour of the system.\nAn approach based on extreme value theory is used for this\nboundary prediction process. Then a sliding window is used to\ntest for anomalous series within the newly arrived collection\nof series. Feature based representation of time series is used\nas the input to the model. To cope with concept drift, the\nforecast boundary for the system's typical behaviour is updated\nperiodically. More details regarding the algorithm can be\nfound in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et\nal. (2019) .", "BugReports": "https://github.com/pridiltal/oddstream/issues", "License": "GPL-3", "LazyData": "true", "RoxygenNote": "7.1.0", "Encoding": "UTF-8", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://robjhyndman.r-universe.dev", "Date/Publication": "2020-04-02 09:12:41 UTC", "RemoteUrl": "https://github.com/pridiltal/oddstream", "RemoteRef": "HEAD", "RemoteSha": "acb95325e27b475fb6c91151cf6f3d18ca622563", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-20 11:25:22 UTC", "User": "root" }, "Author": "Priyanga Dilini Talagala [aut, cre],\nRob J. Hyndman [ths],\nKate Smith-Miles [ths]", "_user": "robjhyndman", "_type": "src", "_file": "oddstream_0.5.1.tar.gz", "_fileid": "https://r2.ropensci.org/af3cce035a24b0811859f85f06befdf3a08b5167d61c95895f85f1db3a6ede29", "_filesize": 2717951, "_sha256": "af3cce035a24b0811859f85f06befdf3a08b5167d61c95895f85f1db3a6ede29", "_expires": "2027-01-28T11:48:46.000Z", "_created": "2026-10-20T11:25:22.000Z", "_published": "2026-10-20T11:48:48.323Z", "_upstream": "https://github.com/pridiltal/oddstream", "_status": "success", "_maintainer": { "name": "Priyanga Dilini Talagala", "email": "pritalagala@gmail.com", "login": "pridiltal", "description": "PhD in Statistics, 2019 🌏 📷📈 🏛\n\n\n", "uuid": 20311818 }, "_jobs": [ { "job": 118601416625, "time": 168, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "12261271137" }, { "job": 118601416609, "time": 271, "config": "linux-release-x86_64", "r": "4.6.1", "check": "NOTE", "artifact": "12260767471" }, { "job": 118601416696, 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