CONSTRUCTION OF MULTIVARIATE GENERALIZED SPACE TIME AUTOREGRESSIVE (MULTIVARIATE GSTAR) MODEL WITH MODIFIED WEIGHT MATRIX
Since COVID-19 first appeared in China, the virus has grown so fast and spread around the world in a matter of months that the WHO (World Health Organization) has designated the COVID-19 virus as a global pandemic. The unrest experienced by all countries requires their governments to take policie...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/61882 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Since COVID-19 first appeared in China, the virus has grown so fast and spread
around the world in a matter of months that the WHO (World Health Organization)
has designated the COVID-19 virus as a global pandemic. The unrest experienced
by all countries requires their governments to take policies to suppress the
transmission of the virus from one location to another. A space-time model can be
designed to predict COVID-19 cases in various locations with various observation
variables. The Multivariate GSTAR model was constructed to predict several
variables and locations simultaneously with a sequence of observations based on
time. The weight matrix was built using the inverse of distance and correlation
between cases at each observation location which was then modified. The model
that has been obtained is then estimated using the least-square method. The process
stationary examination used residual test, parameter matrix eigenvalue approach,
and inverse autocovariance matrix approach. The model is applied to predict
COVID-19 infected, death, and recovery cases for all provinces on the island of
Sumatra. The results showed that the Multivariate GSTAR (1;1) model was very
well applied in predicting death cases in the province of Bangka Belitung Islands
and Bengkulu. |
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