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  "Title": "Forecasting Models for Intermittent Time Series",
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  "Authors@R": "c(person(given  =\"Stefano\",\nfamily =\"Damato\",\nrole   = c(\"aut\", \"cre\", \"cph\"),\nemail  = \"stefanodamato128@gmail.com\",\ncomment = c(ORCID = \"0009-0001-0225-3736\")),\nperson(given = \"Lorenzo\",\nfamily = \"Zambon\",\nrole = c(\"aut\"),\nemail = \"lorenzo.zambon@idsia.ch\",\ncomment = c(ORCID = \"0000-0002-8939-993X\")),\nperson(given = \"Dario\",\nfamily = \"Azzimonti\",\nrole = c(\"aut\"),\nemail = \"dario.azzimonti@gmail.com\",\ncomment = c(ORCID = \"0000-0001-5080-3061\")))",
  "Description": "Extends the 'fable' framework to support forecasting\nmethods specifically designed for intermittent time series\ndata, where demand occurs sporadically with many zero values.\nAll methods produce probabilistic forecasts returned as\n'distributional' objects. The returned forecasts can be used to\nevaluate accuracy, plot and print the results seamlessly with\n'fable'. The methods include: Harvey, Fernandes (1989)\n<doi:10.1080/07350015.1989.10509750>, Willemain, Smart, Schwarz\n(2004) <doi:10.1016/S0169-2070(03)00013-X>, Zhou, Viswanathan\n(2011) <doi:10.1016/j.ijpe.2010.09.021>, Snyder, Ord, Beaumont\n(2012) <doi:10.1016/j.ijforecast.2011.03.009>, Kolassa (2016)\n<doi:10.1016/j.ijforecast.2015.12.004>, Hasni, Aguir, Babai,\nJemai (2019) <doi:10.1080/00207543.2018.1424375>, Damato,\nAzzimonti, Corani (2025)\n<doi:10.1016/j.ijforecast.2025.10.001>, Sbrana (2025)\n<doi:10.1080/01605682.2025.2569661>.",
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  "Date/Publication": "2026-06-09 07:13:20 UTC",
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  "Author": "Stefano Damato [aut, cre, cph] (ORCID:\n<https://orcid.org/0009-0001-0225-3736>),\nLorenzo Zambon [aut] (ORCID: <https://orcid.org/0000-0002-8939-993X>),\nDario Azzimonti [aut] (ORCID: <https://orcid.org/0000-0001-5080-3061>)",
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      "title": "Hurdle-Shifted Poisson Exponential Smoothing",
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      "title": "Markov Chain Model with Random Walk dynamic",
      "topics": [
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      "page": "model_sum.BETANBB",
      "title": "Return model name",
      "topics": [
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      "page": "model_sum.EMPDISTR",
      "title": "Return model name",
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      "page": "model_sum.GAMPOISB",
      "title": "Return model name",
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      "page": "model_sum.HSPES",
      "title": "Return model name",
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      "page": "model_sum.MARWAL",
      "title": "Return model name",
      "topics": [
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    {
      "page": "model_sum.NEGBINES",
      "title": "Return model name",
      "topics": [
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    {
      "page": "model_sum.STATICDISTR",
      "title": "Return model name",
      "topics": [
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    },
    {
      "page": "model_sum.TWEES",
      "title": "Return model name",
      "topics": [
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      "page": "model_sum.VZ",
      "title": "Return model name",
      "topics": [
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      "page": "model_sum.WSS",
      "title": "Return model name",
      "topics": [
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    {
      "page": "NEGBINES",
      "title": "Negative Binomial Exponential Smoothing",
      "topics": [
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      "page": "raf",
      "title": "RAF Spare Parts Demand Dataset",
      "topics": [
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    {
      "page": "residuals.BETANBB",
      "title": "Extract residuals from a BETANBB model",
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      "page": "residuals.EMPDISTR",
      "title": "Extract residuals from an EMPDISTR model",
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      "title": "Extract residuals from a HSPES model",
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      "page": "residuals.MARWAL",
      "title": "Extract residuals from a MARWAL model",
      "topics": [
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    {
      "page": "residuals.NEGBINES",
      "title": "Extract residuals from a NEGBINES model",
      "topics": [
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    },
    {
      "page": "residuals.STATICDISTR",
      "title": "Extract residuals from a STATICDISTR model",
      "topics": [
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      "page": "residuals.TWEES",
      "title": "Extract residuals from a TWEES model",
      "topics": [
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      "page": "residuals.WSS",
      "title": "Extract residuals from a WSS model",
      "topics": [
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      "page": "STATICDISTR",
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