Neural AutoRegressive Fractionally Integrated Moving Average Model
Overview
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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 5
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 40%
- Documented parameters
- 98%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing narfima adds 32 packages (20 with compiled code, 25 MB of source tarballs). Heaviest: ggplot2 6.0 MB, Boom 2.5 MB, colorspace 2.0 MB.
Code & Tests
People & History
- RR 4.6.0 released · 2026-04-24
- 0.1.0Latest2025-09-21 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-09-21
- Total releases
- 1 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 11 KB
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