{
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  "Title": "Multi-Seasonal ARIMA Models",
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  "Description": "Fit, forecast and simulate autoregressive integrated\nmoving average (ARIMA) models with more than one seasonal\ncomponent, following Lisi and Grigoletto (2025)\n<doi:10.1002/for.3283>. 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) <doi:10.48550/arXiv.2609.32381>,\nseasonal correlograms, forecasts with prediction intervals, and\nsimulation from a model specification or from a fitted model\nare also provided.",
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  "Date/Publication": "2026-11-13 11:10:00 UTC",
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  "Author": "Daniele Girolimetto [aut, cre, cph] (ORCID:\n<https://orcid.org/0000-0001-9387-1232>),\nFrancesco Lisi [aut] (ORCID: <https://orcid.org/0000-0002-8749-8889>),\nMatteo Grigoletto [aut] (ORCID:\n<https://orcid.org/0000-0001-9881-2039>)",
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