Comparative Evaluation of SARIMA, Prophet, and Holt–Winters Algorithms for Forecasting Intentional Homicide Patterns in Ecuador (2020–2024)

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Abstract
Ecuador experienced one of the fastest escalations of lethal violence in Latin America, with the intentional homicide rate rising from 7.8 per 100,000 inhabitants in 2020 to 47.25 in 2023. This study develops a replicable, algorithm-selection framework for forecasting the temporal evolution of four key patterns of intentional homicide (monthly case volume, firearm share, mean victim age, and geographic concentration in the province of Guayas) using official open data comprising 24,000+ incident records (January 2020–December 2024). Individual incidents were aggregated into monthly time series, and three forecasting algorithms with distinct inductive biases (SARIMA, Prophet, and Holt–Winters exponential smoothing) were compared under an out-of-sample protocol that reserves the 12 months of 2024 as a test set, using MAE, RMSE, and MAPE. No single algorithm dominated: SARIMA was the most accurate for the volume and geographic series, Holt–Winters for the firearm-share series (MAPE 2.85%), and Prophet for the volatile mean-age series (MAPE 5.73%). Re-trained on the full sample, the selected models project a five-year horizon (2025–2029) in which homicides stabilize at historically high levels, roughly four times the 2020 baseline, with firearm involvement consolidating above 85% and violence remaining concentrated in Guayas. The results support pattern-specific algorithm selection for criminological forecasting and provide a quantitative baseline for security policy evaluation in Ecuador.
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