TDSTNN (version 0.1.0)

STNN: Spatio-Temporal Neural Network

Description

The STNN function fits a Time-Delay Spatio-Temporal Neural Network model for space-time time series data.

Usage

STNN(data,lag, weight0, weight1,hs, h)

Value

Model Summary

Summary of the fitted STNN

Fitted values

Fitted values of STNN

Forecasted values

h step ahead forecasted values employing STNN

Arguments

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.

Author

Mrinmoy Ray, Rajeev Ranjan Kumar, Kanchan Sinha, K. N. Singh

Details

This function enables you to apply the Time-delay Spatio-Temporal model for analyzing space-time series data.

References

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).

See Also

ANN

Examples

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)