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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=TDSTNN to link to this page.
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