Article data titled: Temporal Prediction of Illicit Drug Trafficking and Micro-Distribution Arrests Using ARIMA Models in Peru

This study aims to develop and evaluate temporal forecasting models based on the AutoRegressive Integrated Moving Average (ARIMA) methodology to predict trends in arrests associated with illicit drug trafficking and micro-distribution activities in Peru. The research is grounded in the growing need for data-driven approaches that support criminal intelligence, public security policies, and strategic decision-making in the prevention and control of drug-related offenses.A quantitative, longitudinal, and predictive research design was employed using secondary data obtained from official national crime and law enforcement records in Peru. The dataset included temporal series of arrests linked to illicit drug trafficking and micro-commercialization over multiple consecutive periods. Prior to model estimation, exploratory analyses, stationarity testing, differencing procedures, and autocorrelation diagnostics were conducted to ensure the adequacy of the time-series structure.Subsequently, several ARIMA configurations were evaluated to identify the model with the best predictive performance based on statistical fit indicators and forecasting accuracy metrics. The study seeks to provide empirical evidence regarding temporal patterns of drug-related criminal activity and to contribute methodological insights into the application of econometric and predictive analytics models within the field of public security and criminology.The findings are expected to support governmental institutions, law enforcement agencies, and policymakers by offering reliable forecasting tools for anticipating fluctuations in drug-related arrests, optimizing resource allocation, and strengthening preventive intervention strategies in high-risk contexts.