## 1 Introduction  
The nature of criminal activity is complex and constantly changing, making accurate prediction a significant challenge for experts in criminology, data science, and public policy. Unlike static datasets that might reflect unchanging phenomena, crime patterns are subject to continuous and often unpredictable evolution. This includes not only shifts influenced by broad socioeconomic conditions and demographic changes but also the rapid emergence of novel criminal methodologies, such as sophisticated cybercrimes and intricate types of financial fraud. Traditional statistical time series models, while historically valuable for capturing established temporal trends and predictable seasonalities in crime data, frequently fall short when confronted with these abrupt shifts, unforeseen events, or the profound nonlinear relationships that increasingly characterize contemporary criminal behavior. Their reliance on assumptions of linearity and stationarity limits their capacity to fully decipher the intricate causal webs driving modern crime. Conversely, advanced machine learning models, particularly those capable of disceming highly complex and nonlinear patterns, offer significant predictive power. However, many of these powerful algorithms often operate as \"black boxes,\" providing accurate forecasts without transparently revealing the underlying logic or the specific influence of individual factors on their outputs. This opacity poses a substantial hurdle for actionable insights in public safety, where understanding why a risk is predicted is as important as the prediction itself, particularly for informing strategic resource allocation, policy adjustments, and even legal defensibility. The primary challenge in developing reliable crime prediction models is ensuring both the highest possible accuracy and maintaining interpretability. This complex challenge is precisely what hybrid approaches like the ARIMA-ANN model aim to solve by combining statistical precision with machine learning's adaptability. These models do not just merge methods; instead, a unified system is created that simultaneously handles the unique features of crime data. The ARIMA component effectively identifies and models linear temporal relationships and long-term trends within the data. In contrast, the ANN component is tailored to capture and learn from the complex, nonlinear residual patterns that remain unexplained by ARIMA. This integrated strategy enhances predictive accuracy by addressing both linear and nonlinear dynamics, while also allowing for more refined interpretation of the underlying factors influencing crime risk. Beyond the task of forecasting, this approach clarifies the variables most responsible for changes in model outputs and provides a robust foundation for continuous adaptation to evolving patterns in criminal activity. As a result, the methodology supports greater relevance, operational value, and transparency in data-driven decision-making for law enforcement and urban planning [1; 2]. Recent studies have shown that human behavior can be anticipated using advanced data-driven methods. For instance, studies demonstrate how sensor data can predict changes in individual activity patterns [3]. In recent years, classic statistical methods, such as Autoregressive Integrated Moving Average (ARIMA), have given way to machine learning techniques and mixed approaches. These new tools help capture both linear and nonlinear patterns in complex social systems [4; 5]. The literature points out that single-method approaches struggle to explain the true complexity of crime. Crime rates are influenced by a mix of social, economic, demographic, and environmental factors [6; 7]. While ARIMA models can track trends over time and spot seasonal effects, they struggle when data show sudden shifts, unexpected changes, or hidden nonlinearities [8]. Artificial neural networks (ANNs), on the other hand, can catch these nonlinear patterns, although sometimes at the cost of less interpretability and a higher risk of overfitting [9].