{ "_id": "6a0f669bacfb0bcc41c5d4ea", "Package": "narfima", "Title": "Neural AutoRegressive Fractionally Integrated Moving Average\nModel", "Version": "0.1.0", "Authors@R": "c(\nperson(\"Tanujit\", \"Chakraborty\", email = \"tanujit.chakraborty@sorbonne.ae\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"https://orcid.org/0000-0002-3479-2187\")),\nperson(\"Donia\", \"Besher\", email = \"donia.a.besher@gmail.com\",\nrole = c(\"aut\",\"cre\",\"cph\"),\ncomment = c(ORCID = \"https://orcid.org/0009-0008-8314-1576\")),\nperson(\"Madhurima\", \"Panja\", email = \"madhurima.panja@sorbonne.ae\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"https://orcid.org/0009-0004-7467-2456\")),\nperson(\"Shovon\", \"Sengupta\", email = \"shovon.sengupta@fmr.com\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"https://orcid.org/0000-0003-2169-7364\"))\n)", "Description": "Methods and tools for forecasting univariate time series\nusing the NARFIMA (Neural AutoRegressive Fractionally\nIntegrated Moving Average) model. It combines neural networks\nwith fractional differencing to capture both nonlinear patterns\nand long-term dependencies. The NARFIMA model supports seasonal\nadjustment, Box-Cox transformations, optional exogenous\nvariables, and the computation of prediction intervals. In\naddition to the NARFIMA model, this package provides\nalternative forecasting models including NARIMA (Neural ARIMA),\nNBSTS (Neural Bayesian Structural Time Series), and NNaive\n(Neural Naive) for performance comparison across different\nmodeling approaches. The methods are based on algorithms\nintroduced by Chakraborty et al. 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