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STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.
| Version: | 0.1.0 |
| Depends: | R (≥ 4.2.3), nnet |
| Published: | 2024-05-26 |
| DOI: | 10.32614/CRAN.package.TDSTNN |
| Author: | Mrinmoy Ray [aut, cre], Rajeev Ranjan Kumar [aut, ctb], Kanchan Sinha [aut, ctb], K. N. Singh [aut, ctb] |
| Maintainer: | Mrinmoy Ray <mrinmoy4848 at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | TDSTNN results |
| Reference manual: | TDSTNN.pdf |
| Package source: | TDSTNN_0.1.0.tar.gz |
| Windows binaries: | r-devel: TDSTNN_0.1.0.zip, r-release: TDSTNN_0.1.0.zip, r-oldrel: TDSTNN_0.1.0.zip |
| macOS binaries: | r-release (arm64): TDSTNN_0.1.0.tgz, r-oldrel (arm64): TDSTNN_0.1.0.tgz, r-release (x86_64): TDSTNN_0.1.0.tgz, r-oldrel (x86_64): TDSTNN_0.1.0.tgz |
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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.