| Title: | Time Delay Spatio Temporal Neural Network |
|---|---|
| Description: | 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>. |
| Authors: | Mrinmoy Ray [aut, cre], Rajeev Ranjan Kumar [aut, ctb], Kanchan Sinha [aut, ctb], K. N. Singh [aut, ctb] |
| Maintainer: | Mrinmoy Ray <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2024-12-23 06:17:39 UTC |
| Source: | CRAN |
The STNN function fits a Time-Delay Spatio-Temporal Neural Network model for space-time time series data.
STNN(data,lag, weight0, weight1,hs, h)STNN(data,lag, weight0, weight1,hs, h)
data |
Spatio-temporal time series (ts) data. |
lag |
Number of time lag(s). |
weight0 |
Zero-order weight matrix. |
weight1 |
First-order weight matrix. |
hs |
Number of hidden layer(s). |
h |
The forecast horizon. |
This function enables you to apply the Time-delay Spatio-Temporal model for analyzing space-time series data.
Model Summary |
Summary of the fitted STNN |
Fitted values |
Fitted values of STNN |
Forecasted values |
h step ahead forecasted values employing STNN |
Mrinmoy Ray, Rajeev Ranjan Kumar, Kanchan Sinha, K. N. Singh
Saha, A., Singh, K.N., Ray, M. et al. A hybrid spatio-temporal modelling: an application to space-time rainfall forecasting. Theor Appl Climatol 142, 1271–1282 (2020).
ANN
ts.sim1 <- 50+arima.sim(list(order = c(1,1,0), ar = 0.7), n = 100) ts.sim2<-70+arima.sim(list(order = c(1,1,0), ar = 0.8), n = 100) weight0=diag(1, 2, 2) weight1=matrix(c(0, 1, 1, 0), nrow= 2, ncol = 2, byrow = TRUE) zz=as.matrix(cbind(ts.sim1,ts.sim2)) data=zz lag=1 hs=2 h=5 STNN(data,lag,weight0,weight1,hs,h)ts.sim1 <- 50+arima.sim(list(order = c(1,1,0), ar = 0.7), n = 100) ts.sim2<-70+arima.sim(list(order = c(1,1,0), ar = 0.8), n = 100) weight0=diag(1, 2, 2) weight1=matrix(c(0, 1, 1, 0), nrow= 2, ncol = 2, byrow = TRUE) zz=as.matrix(cbind(ts.sim1,ts.sim2)) data=zz lag=1 hs=2 h=5 STNN(data,lag,weight0,weight1,hs,h)