Space Time Model in Missing Value Based on Google Trends Data: Gold Price during Covid-19

Fadhlul Mubarak (Sharia Accounting, UIN Sulthan Thaha Saifuddin Jambi)
Vinny Yuliani Sundara (Department of Statistics, UIN Sulthan Thaha Saifuddin Jambi)
Nurniswah (Department of Social Studies Education, UIN Fatmawati Sukarno Bengkulu)
Atilla Aslanargun (Department of Statistics, Eskisehir Technical University)

UNP Journal of Statistics and Data Science
Vol. 4 No. 3 (2026): UNP Journal of Statistics and Data Science

In certain cases, there are data that have a missing value problem. One of them is gold price data from the World Gold Council. The main purpose of this study was to predict gold prices in Austria, India, South Korea, and Turkiye during Covid-19 using the best space-time model on the data. The models that have been used in this research were generalized space-time autoregressive integrated with exogenous variable (GSTARX) and generalized space time autoregressive integrated with exogenous variable (GSTARIX). Before using these models, the last observation carried forward (LOCF) imputation technique solved the missing value problem. In addition, google trends data has been used as an alternative to the spatial weighting matrix and exogenous variables in the two models. And the google trend categories that have been used were google shopping, image search, news search, web search, and youtube search. Based on the smallest mean absolute percentage error (MAPE was 4.3%), GSTARX model in which the weighting matrix has been derived from image search. Relatively speaking, the results of forecasting gold prices in Austria was constant, India was declining, South Korea was declining significantly and Turkiye was incresing significantly.