TDSTNN: Time Delay Spatio Temporal Neural Network

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: NA
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:

Reference manual: TDSTNN.pdf

Downloads:

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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