Analysis and Prediction of the Occurrence of an Earthquake Using ARIMA and Statistical Tests

Kumoro, Rabbani Nur
Fattima, Audrey Shafira
Susatyo, William Hilmy
Fudholi, Dzikri Rahadian

IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Vol 18, No 3 (2024): July

Earthquakes present significant risks to both human safety and infrastructure, emphasizing the need for precise prediction models to minimize their adverse effects. This study seeks to tackle the challenge of accurately forecasting the occurrence time of earthquakes by utilizing the LANL Earthquake dataset, which comprises seismic signals from a laboratory model emulating tectonic faults. In this study, we employed the ARIMA model and compared it with Linear Regression to predict earthquake occurrences. Our findings demonstrate that the ARIMA (1,1,1) model surpasses other models, achieving the lowest MAE of 0.110628. The validity of the model's assumptions is confirmed through the Ljung-Box and Jarque-Bera tests, which verify the absence of autocorrelation and the normal distribution of residuals, respectively.