{ "_id": "6af5bd01be2268deb4a3d6b7", "Package": "msarima", "Title": "Multi-Seasonal ARIMA Models", "Version": "0.1.0", "Authors@R": "c(person(given = \"Daniele\",\nfamily = \"Girolimetto\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"daniele.girolimetto@unipd.it\",\ncomment = c(ORCID = \"0000-0001-9387-1232\")),\nperson(given = \"Francesco\",\nfamily = \"Lisi\",\nrole = c(\"aut\"),\nemail = \"francesco.lisi@unipd.it\",\ncomment = c(ORCID = \"0000-0002-8749-8889\")),\nperson(given = \"Matteo\",\nfamily = \"Grigoletto\",\nrole = c(\"aut\"),\nemail = \"matteo.grigoletto@unipd.it\",\ncomment = c(ORCID = \"0000-0001-9881-2039\")))", "Description": "Fit, forecast and simulate autoregressive integrated\nmoving average (ARIMA) models with more than one seasonal\ncomponent, following Lisi and Grigoletto (2025)\n. Series observed at high frequency often\ndisplay several seasonal cycles at once, such as the daily and\nthe weekly pattern of hourly data, which a single seasonal\nARIMA operator cannot represent. Estimation is performed by\nconditional maximum likelihood through numerical optimisation,\nwith support for fixed coefficients and a constant term.\nMulti-seasonal Ljung-Box and Monti portmanteau tests, following\nGrigoletto and Lisi (2026) ,\nseasonal correlograms, forecasts with prediction intervals, and\nsimulation from a model specification or from a fitted model\nare also provided.", "License": "GPL-3", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "URL": "https://github.com/danigiro/msarima,\nhttps://danigiro.github.io/msarima/", "BugReports": "https://github.com/danigiro/msarima/issues", "LazyData": "true", "NeedsCompilation": "no", "Packaged": { "Date": "2026-11-12 13:14:53 UTC", "User": "root" }, "Author": "Daniele Girolimetto [aut, cre, cph] (ORCID:\n), Francesco Lisi [aut]\n(ORCID: ), Matteo\nGrigoletto [aut] (ORCID:\n)", "Maintainer": "Daniele Girolimetto ", "Repository": "https://cran.r-universe.dev", 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