Predictive modeling of unrest situation in a Southern province of Thailand using machine learning models

The southern provinces of Thailand continue to experience persistent unrest and insurgency, creating an urgent need for reliable forecasting methods to support decision-making. This study aims to improve the forecasting of unrest and insurgency cases by evaluating alternative model selection approaches using unrest databases. We analyzed records of deaths, incidents, and injuries from 2004 to 2019 across all 12 districts of Pattani province, employing Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Autoregressive Integrated Moving Average (ARIMA) models. Forecasting accuracy was assessed using the mean square error criterion. The findings indicate substantial variation in the monthly time series of deaths, incidents, and injuries, with the ARIMA model consistently producing the most accurate forecasts for injuries across districts. These results underscore the importance of model choice when applying forecasting techniques to conflict-related datasets. In conclusion, ARIMA offers a robust and practical approach for anticipating short-term unrest trends. The study has practical implications for policymakers, security agencies, and researchers seeking evidence-based strategies to anticipate and mitigate the effects of insurgency in southern Thailand.