Using Time Series Analysis to Predict Earthquake

One of the most disastrous natural disasters is an earthquake; it has the potential to cause mass-scale destruction and extensive loss of human lives. An earthquake is a sudden, violent movement of the earth's surface, produced by the movement of tectonic plates at faults in the earth's crust. The energy thus released travels in seismic waves. The earthquakes are totally unpredictable; therefore, safe buildings become more important in regions where the seismic events are more frequent. Prediction of earthquakes through time series analysis is one of the most challenging tasks and has, over time, shown promising improvements. Some of the most common techniques applied in predicting earthquakes are AutoRegressive Integrated Moving Average, Exponential Smoothing, Moving Average, Generalized Autoregressive Conditional Heteroskedasticity, and Seasonal AutoRegressive Integrated Moving Average with eXogenous factors, all having some unique strengths in making better predictions. These models work by analyzing past seismic data for trends and patterns that could indicate the likelihood of a future earthquake. This paper aimed at establishing which of these models performs better in predicting earthquakes by analyzing them based on accuracy and reliability. ARIMA has, over time, emerged as the best since it found better predictive power in earthquake prediction. Its admirable performance may potentially enhance earthquake prediction and, in effect, disaster preparedness. This will require continued research into the area so that predictive capabilities can be further refined and improved to really help lessen the impacts of earthquakes on vulnerable communities.