{ "_id": "6ac77111de46216d5edcfeba", "Package": "InterNL", "Type": "Package", "Title": "Time Series Intervention Model Using Non-Linear Function", "Version": "0.1.0", "Authors@R": "c(person(\"Dr. Amrit Kumar\", \"Paul\", role = \"aut\"),\nperson(\"Dr. Md\", \"Yeasin\", role = c(\"aut\",\"cre\"), email = \"yeasin.iasri@gmail.com\"),\nperson(\"Dr. Ranjit Kumar\", \"Paul\", role = \"aut\"),\nperson(\"Mr. Subhankar\", \"Biswas\", role = \"aut\"),\nperson(\"Dr. HS\", \"Roy\", role = \"aut\"),\nperson(\"Dr. Prakash\", \"Kumar\", role = \"aut\")\n)", "Author": "Dr. Amrit Kumar Paul [aut], Dr. Md Yeasin [aut, cre], Dr.\nRanjit Kumar Paul [aut], Mr. Subhankar Biswas [aut], Dr. HS Roy\n[aut], Dr. Prakash Kumar [aut]", "Maintainer": "Dr. Md Yeasin ", "Description": "Intervention analysis is used to investigate structural\nchanges in data resulting from external events. Traditional\ntime series intervention models, viz. Autoregressive Integrated\nMoving Average model with exogeneous variables (ARIMA-X) and\nArtificial Neural Networks with exogeneous variables (ANN-X),\nrely on linear intervention functions such as step or ramp\nfunctions, or their combinations. In this package, the\nGompertz, Logistic, Monomolecular, Richard and Hoerl function\nhave been used as non-linear intervention function. The\nequation of the above models are represented as: Gompertz: A *\nexp(-B * exp(-k * t)); Logistic: K / (1 + ((K - N0) / N0) *\nexp(-r * t)); Monomolecular: A * exp(-k * t); Richard: A + (K -\nA) / (1 + exp(-B * (C - t)))^(1/beta) and Hoerl:\na*(b^t)*(t^c).This package introduced algorithm for time series\nintervention analysis employing ARIMA and ANN models with a\nnon-linear intervention function. 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