# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "narfima" in publications use:' type: software license: GPL-3.0-only title: 'narfima: Neural AutoRegressive Fractionally Integrated Moving Average Model' version: 0.1.0 doi: 10.32614/CRAN.package.narfima abstract: Methods and tools for forecasting univariate time series using the NARFIMA (Neural AutoRegressive Fractionally Integrated Moving Average) model. It combines neural networks with fractional differencing to capture both nonlinear patterns and long-term dependencies. The NARFIMA model supports seasonal adjustment, Box-Cox transformations, optional exogenous variables, and the computation of prediction intervals. In addition to the NARFIMA model, this package provides alternative forecasting models including NARIMA (Neural ARIMA), NBSTS (Neural Bayesian Structural Time Series), and NNaive (Neural Naive) for performance comparison across different modeling approaches. The methods are based on algorithms introduced by Chakraborty et al. (2025) . authors: - family-names: Chakraborty given-names: Tanujit email: tanujit.chakraborty@sorbonne.ae orcid: https://orcid.org/0000-0002-3479-2187 - family-names: Besher given-names: Donia email: donia.a.besher@gmail.com orcid: https://orcid.org/0009-0008-8314-1576 - family-names: Panja given-names: Madhurima email: madhurima.panja@sorbonne.ae orcid: https://orcid.org/0009-0004-7467-2456 - family-names: Sengupta given-names: Shovon email: shovon.sengupta@fmr.com orcid: https://orcid.org/0000-0003-2169-7364 repository: https://cran.r-universe.dev commit: 6120e4e86f4aaeae92262d65740f92a862116732 date-released: '2025-09-21' contact: - family-names: Besher given-names: Donia email: donia.a.besher@gmail.com orcid: https://orcid.org/0009-0008-8314-1576