Temporal prediction of arrests for illicit drug trafficking and micro-distribution using ARIMA models in Peru

<ns3:p> Introduction Illicit drug trafficking and micro-distribution represent persistent challenges affecting public security, institutional governance, and social dynamics in Peru. In this context, the aim of the study was to temporally predict trends in arrests related to illicit drug trafficking and micro-distribution using ARIMA models in Peru. Methods The research was conducted under a quantitative approach with a non-experimental, descriptive-predictive, and retrospective longitudinal design. Official administrative records from the Ministry of the Interior of Peru corresponding to the 2020–2024 period were used. Statistical analysis included descriptive and correlational procedures, as well as time-series modeling through ARIMA models, complemented by validation indicators such as RMSE, MAE, MAPE, BIC, and the Ljung-Box Q test <ns3:bold>.</ns3:bold> Results The findings revealed a marked predominance of male arrests, accounting for 90.11% of the analyzed records. Time-series analyses showed greater fluctuations and variability among men, whereas female arrests exhibited a more stable pattern over time. The ARIMA models achieved adequate levels of fit and predictive accuracy, identifying strong positive associations between observed and forecasted records. Furthermore, temporal projections estimated an upward trend in arrests through 2030, particularly within the male population. Conclusion ARIMA models constitute useful tools for anticipating criminal trends related to illicit drug trafficking and micro-distribution in Peru. The findings contribute to strengthening prevention strategies, surveillance systems, and institutional planning from a predictive perspective supported by statistical evidence. </ns3:p>