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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | TDSTNN_0.1.0.tar.gz |
11.0 KiB |
0.1.0 |
rolling linux/noble R-4.5 | TDSTNN_0.1.0.tar.gz |
10.9 KiB |
0.1.0 |
rolling source/ R- | TDSTNN_0.1.0.tar.gz |
2.1 KiB |
0.1.0 |
latest linux/jammy R-4.5 | TDSTNN_0.1.0.tar.gz |
11.0 KiB |
0.1.0 |
latest linux/noble R-4.5 | TDSTNN_0.1.0.tar.gz |
10.9 KiB |
0.1.0 |
latest source/ R- | TDSTNN_0.1.0.tar.gz |
2.1 KiB |
0.1.0 |
2026-04-26 source/ R- | TDSTNN_0.1.0.tar.gz |
2.1 KiB |
0.1.0 |
2026-04-23 source/ R- | TDSTNN_0.1.0.tar.gz |
2.1 KiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | TDSTNN_0.1.0.zip |
13.7 KiB |
0.1.0 |
2025-04-20 source/ R- | TDSTNN_0.1.0.tar.gz |
2.1 KiB |