{ "_id": "6a104f43acfb0bcc41ca0d54", "Package": "seer", "Type": "Package", "Title": "Feature-Based Forecast Model Selection", "Version": "1.1.8", "Authors@R": "c(\nperson(\"Thiyanga\", \"Talagala\", email = \"tstalagala@gmail.com\", role = c(\"aut\", \"cre\"), comment=c(ORCID = \"0000-0002-0656-9789\")),\nperson(\"Rob J\", \"Hyndman\", role = c(\"ths\", \"aut\"), comment = c(ORCID = \"0000-0002-2140-5352\")),\nperson(\"George\", \"Athanasopoulos\", role = c(\"ths\", \"aut\")))", "Maintainer": "Thiyanga Talagala ", "Description": "A novel meta-learning framework for forecast model\nselection using time series features. Many applications require\na large number of time series to be forecast. Providing better\nforecasts for these time series is important in decision and\npolicy making. We propose a classification framework which\nselects forecast models based on features calculated from the\ntime series. We call this framework FFORMS (Feature-based\nFORecast Model Selection). FFORMS builds a mapping that relates\nthe features of time series to the best forecast model using a\nrandom forest. 'seer' package is the implementation of the\nFFORMS algorithm. For more details see our paper at\n.", "License": "GPL-3", "URL": "https://thiyangt.github.io/seer/", "BugReports": "https://github.com/thiyangt/seer/issues", "Encoding": "UTF-8", "RoxygenNote": "7.2.1", "Config/pak/sysreqs": "libicu-dev libssl-dev", "Repository": "https://thiyangt.r-universe.dev", "Date/Publication": "2022-10-01 06:51:19 UTC", "RemoteUrl": "https://github.com/thiyangt/seer", "RemoteRef": "HEAD", "RemoteSha": "abd4c2aa172809146df850db2da64310fa4ae98e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-10 06:08:53 UTC", "User": "root" }, "Author": "Thiyanga Talagala [aut, cre] (ORCID:\n),\nRob J Hyndman [ths, aut] (ORCID:\n),\nGeorge Athanasopoulos [ths, aut]", "MD5sum": "459866f1f4c576bb3d41a7ac651610b0", "_user": "thiyangt", "_type": "src", "_file": "seer_1.1.8.tar.gz", "_fileid": "2f9917c9e025ae134f23d64a702eeabdb42249ba13358bbed944aaf49b37e93b", "_filesize": 175122, "_sha256": "2f9917c9e025ae134f23d64a702eeabdb42249ba13358bbed944aaf49b37e93b", "_created": 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white noise process", "topics": [ "accuracy_wn" ] }, { "page": "acf_seasonalDiff", "title": "Autocorrelation coefficients based on seasonally differenced series", "topics": [ "acf_seasonalDiff" ] }, { "page": "acf5", "title": "Autocorrelation-based features", "topics": [ "acf5" ] }, { "page": "build_rf", "title": "build random forest classifier", "topics": [ "build_rf" ] }, { "page": "cal_features", "title": "Calculate features for new time series instances", "topics": [ "cal_features" ] }, { "page": "cal_m4measures", "title": "Mean of MASE and sMAPE", "topics": [ "cal_m4measures" ] }, { "page": "cal_MASE", "title": "Mean Absolute Scaled Error(MASE)", "topics": [ "cal_MASE" ] }, { "page": "cal_medianscaled", "title": "scale MASE and sMAPE by median", "topics": [ "cal_medianscaled" ] }, { "page": "cal_sMAPE", "title": "symmetric Mean Absolute Pecentage Error(sMAPE)", "topics": [ "cal_sMAPE" ] }, { "page": "cal_WA", "title": "Weighted Average", "topics": [ "cal_WA" ] }, { "page": 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