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.
- Version0.1.0
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release05/26/2024
Team
Mrinmoy Ray
Rajeev Ranjan Kumar
Show author detailsRolesAuthor, ContributorKanchan Sinha
Show author detailsRolesAuthor, ContributorK. N. Singh
Show author detailsRolesAuthor, Contributor
Insights
Last 30 days
This package has been downloaded 101 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 3 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 1,084 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Jun 01, 2024 with 44 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Depends1 package