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The Intuitionistic Fuzzy Autoregressive Integrated Moving Average model offers advanced time series forecasting by integrating intuitionistic fuzzy sets with ARIMA. It enhances prediction accuracy by handling uncertainties and hesitations more effectively than traditional models, making it ideal for complex, real-world data applications. The package has been developed using the algorithm of Yolcu and Yolcu (2023) <doi:10.1016/j.eswa.2022.119336>.
| Version: | 0.1.0 |
| Imports: | stats, forecast |
| Suggests: | testthat (≥ 3.0.0) |
| Published: | 2026-10-29 |
| DOI: | 10.32614/CRAN.package.IFARIMA |
| Author: | Anita Sarkar [aut], Ankit Kumar Singh [aut], Md Yeasin [aut, cre], Ranjit Kumar Paul [aut], Amrit Kumar Paul [aut], Pushkar Bora [aut] |
| Maintainer: | Md Yeasin <yeasin.iasri at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | IFARIMA results |
| Reference manual: | IFARIMA.html , IFARIMA.pdf |
| Package source: | IFARIMA_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: IFARIMA_0.1.0.zip, r-oldrel: IFARIMA_0.1.0.zip |
| macOS binaries: | r-release (arm64): IFARIMA_0.1.0.tgz, r-oldrel (arm64): IFARIMA_0.1.0.tgz, r-release (x86_64): IFARIMA_0.1.0.tgz, r-oldrel (x86_64): IFARIMA_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
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