Cross-Covariance Weight of GSTAR-SUR Model for Rainfall Forecasting in Agricultural Areas

Abstract

<span lang="DE">The use of location weights on the formation of the spatio-temporal<span> </span>model contributes to the accuracy of the model formed. The location weights that are often used include uniform location weight, inverse distance, and cross-correlation normalization. The weight of the location considers the proximity between locations. For data that has a high level of variability, the use of the location weights mentioned above is less relevant. This research was conducted with the aim of obtaining a weighting method that is more suitable for data with high variability. This research was conducted using secondary data derived from 10 daily rainfall data obtained from BMKG Karangploso. The data period used was January 2008 to December 2018. The points of the rain posts studied included the rain post of the Blimbing, Karangploso, Singosari, Dau, and Wagir regions. Based on the results of the research forecasting model obtained is the GSTAR ((1), 1,2,3,12,36) -SUR model. The cross-covariance model produces a better level of accuracy in terms of lower RMSE values and higher R<sup>2</sup> values, especially for Karangploso, Dau, and Wagir areas.</span>

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

“…These articles collectively represent vital insights and areas that need further exploration. The research that has been evaluated demonstrates a high propensity to use GSTARIMA models' capacity to forecast climate-related variables like temperature, precipitation, and air pollutants [19,37,40,[49][50][51]64]. A common thread is the evaluation of model performance metrics, especially MAPE, RMSE, R 2 , and MSE.…”

Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting

“…This research did not extensively explore this gap, such as the influence of temperature, humidity, wind speed, solar radiation, and soil surface humidity, which could affect rainfall patterns across different locations. Climate analysis used more than single-variable constraints, such as predicting air pollution, rainfall, and wind speed separately [10,65,71,74,81]. Although this approach enhanced model accuracy, addressing other complex parameters was necessary to provide different solutions during general climate forecasting for adjacent locations that exhibited a significant correlation.…”

“…GSTARIMA is employed to analyze the distribution of yields and determine pricing based on the transfer function [7][8][9]. These applications extend beyond agriculture, encompassing various sectors [10]. Moreover, unobserved locations can be predicted using GSTAR-Kriging [11,12].…”

Forecasting the Consumer Price Index With Generalized Space-Time Autoregressive Seemingly Unrelated Regression (Gstar-Sur): Compromise Region and Time

“…In 1980 Pfeifer and Deutsch introduced a model that combines time and location interdependence known as Space-Time Autoregressive (STAR) [13]. However, the STAR model has a weakness in the flexibility of parameters that assume that the locations have homogeneous ones, so if the locations have heterogeneous characteristics, the STAR model is not good for use [11], [12], [14]. Developed the Generalized Space-Time AutoSpace-Time (GSTAR) model to address the weaknesses of the STAR model [15].…”