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  "Title": "Fast Time Series Modeling for Seasonal Series with Exogenous\nVariables",
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  "Authors@R": "person(\"Ryan Andrew\", \"Peterson\", \nemail = \"ryan-peterson@uiowa.edu\",\nrole = c(\"aut\", \"cre\", \"cph\"),\ncomment = c(ORCID = \"0000-0002-4650-5798\"))",
  "Description": "An implementation of sparsity-ranked lasso and related\nmethods for time series data. This methodology is especially\nuseful for large time series with exogenous features and/or\ncomplex seasonality. Originally described in Peterson and\nCavanaugh (2022) <doi:10.1007/s10182-021-00431-7> in the\ncontext of variable selection with interactions and/or\npolynomials, ranked sparsity is a philosophy with methods\nuseful for variable selection in the presence of prior\ninformational asymmetry. This situation exists for time series\ndata with complex seasonality, as shown in Peterson and\nCavanaugh (2024) <doi:10.1177/1471082X231225307>, which also\ndescribes this package in greater detail. The sparsity-ranked\npenalization methods for time series implemented in 'fastTS'\ncan fit large/complex/high-frequency time series quickly, even\nwith a high-dimensional exogenous feature set. The method is\nconsiderably faster than its competitors, while often producing\nmore accurate predictions. Also included is a long hourly\nseries of arrivals into the University of Iowa Emergency\nDepartment with concurrent local temperature.",
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  "BugReports": "https://github.com/petersonR/fastTS/issues",
  "Date": "2025-11-29",
  "Repository": "https://petersonr.r-universe.dev",
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