SEASONAL GENERALIZED SPACE-TIME AUTOREGRESSIVE MODELING ANDTRANSFER FUNCTION FOR ANALYZING RAINFALL RISK ON RICE PRODUCTIVITYIN WEST JAVA
Models such as Vector Autoregressive (VAR) and Generalized Space-Time Autoregressive (GSTAR) are commonly used in space-time data analysis. However, these models are unsuitable for analyzing space-time data with seasonal patterns. Consequently, this study develops a Seasonal GSTAR (SGSTAR) model...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/82374 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Models such as Vector Autoregressive (VAR) and Generalized Space-Time Autoregressive
(GSTAR) are commonly used in space-time data analysis. However, these models
are unsuitable for analyzing space-time data with seasonal patterns. Consequently, this
study develops a Seasonal GSTAR (SGSTAR) model to handle space-time data with
seasonal patterns for a variable across multiple locations. Specifically, the spatial order
is restricted to one. The optimal model identified is SGSTAR(1;1)(3;1,1,1)4, which is
then employed to forecast monthly rice productivity for the next 12 months in six districts
in West Java: West Bandung, Bandung, Ciamis, Cirebon, Garut, and Tasikmalaya.
Subsequently, the impact of rainfall on rice productivity in these six districts is analyzed.
A Single-Input Transfer Function model is utilized to predict rice productivity (output
series) using rainfall at each location’s input series. The risk associated with these rice
productivity forecasts and historical data is analyzed using an Early Warning System. |
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