Research Article
GSTARX-SUR Modeling Using Inverse Distance Weighted Matrix and Queen Contiguity Weighted Matrix for Forecasting Cocoa Black Pod Attack in Trenggalek Regency
@INPROCEEDINGS{10.4108/eai.23-10-2019.2293086, author={Arif Ashari and Achmad Efendi and Henny Pramoedyo}, title={GSTARX-SUR Modeling Using Inverse Distance Weighted Matrix and Queen Contiguity Weighted Matrix for Forecasting Cocoa Black Pod Attack in Trenggalek Regency}, proceedings={Proceedings of the 13th International Interdisciplinary Studies Seminar, IISS 2019, 30-31 October 2019, Malang, Indonesia}, publisher={EAI}, proceedings_a={IISS}, year={2020}, month={3}, keywords={gstar gstarx forecasting cocoa black pod}, doi={10.4108/eai.23-10-2019.2293086} }- Arif Ashari
Achmad Efendi
Henny Pramoedyo
Year: 2020
GSTARX-SUR Modeling Using Inverse Distance Weighted Matrix and Queen Contiguity Weighted Matrix for Forecasting Cocoa Black Pod Attack in Trenggalek Regency
IISS
EAI
DOI: 10.4108/eai.23-10-2019.2293086
Abstract
Generalized Space Time Autoregressive (GSTAR) is one of the multivariate time series models considering heterogenic location. One of the GSTAR model developments is GSTARX model with additional exogenous variables. Parameters of GSTAR model could be estimated using Seemingly Unrelated Regression (SUR) approach to cope with the residual model between locations generally related to each other. The model is commonly called GSTARX-SUR. It is applicable in various fields such as agricultural sector. In this research, GSTARX-SUR model was applied to predict cocoa black pod attack in Trenggalek Regency. Rainfall was used as the exogenous variable. One of the characteristics of GSTARX model is the spatial weights. The correct spatial weights in GSTARX model is expected to improve the accuracy result. The research aimed to obtain the best GSTARX model to predict cocoa black pod attack in Trenggalek Regency. The research findings showed that GSTARX-SUR model (1,[1,12])(0,0,0) using inverse distance weighted matrix was the best model. The prediction result was highly accurate, indicated by a small MAPE value less than 15%.
