GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java

Abstract

The spatiotemporal model consists of stationary and non-stationary data, respectively known as the Generalized Space–Time Autoregressive (GSTAR) model and the Generalized Space–Time Autoregressive Integrated (GSTARI) model. The application of this model in forecasting climate with rainfall variables is also influenced by exogenous variables such as humidity, and often the assumption of error is not constant. Therefore, this study aims to design a spatiotemporal model with the addition of exogenous variables and to overcome the non-constant error variance. The proposed model is named GSTARI-X-ARCH. The model is used to predict climate phenomena in West Java, obtained from National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) data. Climate data are big data, so we used knowledge discovery in databases (KDD) in this study. The pre-processing step is collecting and cleaning data. Then, the data mining process with the GSTARI-X-ARCH model follows the Box–Jenkins procedure: model identification, parameter estimation, and diagnostic checking. Finally, the post-processing step for visualization and interpretation of forecast results was conducted. This research is expected to contribute to developing the spatiotemporal model and forecast results as recommendations to the relevant agencies.

Systematic Literature Review on An Integrated Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) Model with Heteroscedastic Error and Kriging Method for Forecasting Climate

“…Kumar et al (2022) used a STARMA-GARCH model to forecast monthly temperatures, resulting in minimal Mean Absolute Percentage Error (MAPE) values in their predictions [36]. Similarly, Monika et al (2022) used the GSTARI-X-ARCH model to forecast rainfall influenced by humidity, showing favorable forecast accuracy [16]. In a different context, Akbar et al (2020) introduced the GSTARMAX model to forecast air pollutants in Surabaya, achieving low Root Mean Square Error (RMSE) values [51].…”

“…It is clear that heteroskedastic errors are critical to climate prediction, and special attention has been paid to using ARCH and GARCH models to address this issue [16,36,57]. Researchers concentrate on achieving higher prediction accuracy, indicated by lower RMSE and MSE values.…”

“…Ditago et al (2016) used the GSTARX-GLS model, with the exogenous variable being calendar variation [15]. Monika et al (2022) developed the GSTARI-X-ARCH model to forecast rainfall with exogenous variables in humidity [16].…”