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narfima: Neural AutoRegressive Fractionally Integrated Moving Average Model

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) <doi:10.48550/arXiv.2509.06697>.

Version: 0.1.0
Imports: forecast, nnet, bsts, stats, utils, withr
Published: 2025-09-21
DOI: 10.32614/CRAN.package.narfima
Author: Tanujit Chakraborty ORCID iD [aut], Donia Besher ORCID iD [aut, cre, cph], Madhurima Panja ORCID iD [aut], Shovon Sengupta ORCID iD [aut]
Maintainer: Donia Besher <donia.a.besher at gmail.com>
License: GPL-3
NeedsCompilation: no
In views: TimeSeries
CRAN checks: narfima results

Documentation:

Reference manual: narfima.html , narfima.pdf

Downloads:

Package source: narfima_0.1.0.tar.gz
Windows binaries: r-devel: narfima_0.1.0.zip, r-release: narfima_0.1.0.zip, r-oldrel: narfima_0.1.0.zip
macOS binaries: r-release (arm64): narfima_0.1.0.tgz, r-oldrel (arm64): narfima_0.1.0.tgz, r-release (x86_64): narfima_0.1.0.tgz, r-oldrel (x86_64): narfima_0.1.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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