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
 [aut],
Donia Besher
[aut, cre, cph],
Madhurima Panja
[aut],
Shovon Sengupta
[aut] |
| Maintainer: |
Donia Besher <donia.a.besher at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| In views: |
TimeSeries |
| CRAN checks: |
narfima results |
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