## 5 Conclusions  
This research compared a hybrid ARIMA-ANN model with several well-known multiclass classifiers to determine which one better predicts monthly crime risk in Galati County, Romania. Our analysis spans eleven years of data and introduces a new method for categorizing crime risk into five distinct groups. This approach helps ensure fairness and reflects real differences in the local population. What stood out is that the ARIMA-ANN hybrid consistently outperformed every other model that was tested. This applied to all main evaluation metrics: accuracy, recall, and prediction stability. The hybrid model was especially good at identifying high-risk months that often follow economic shocks or significant social events. We believe this performance stems from the ARIMA component, which models regular cycles and trends in the crime data, while the ANN part detects irregular changes and more complex relationships between various factors. From a policy viewpoint, these results are important. With the help of the hybrid model, authorities could go beyond just reacting to crime after it occurs. They could plan ahead, spot periods of increased risk, and allocate police or social workers more effectively. Since the model divides crime risk into clear categories, it provides decision-makers with an accurate view of where resources are most necessary. The transparent and repeatable steps in building and validating the model further support public trust, as any decisions made with this model can be explained and justified if questioned. In Galati County, our model delivered stronger and more reliable predictions than standard machine learning models. However, before making broad conclusions, the model should be tested in other counties or countries, especially where local conditions or available data may vary. This represents an important next step for future studies. This study significantly contributes to the field of predictive crime analytics by integrating a hybrid ARIMA and ANN model that captures both linear and complex nonlinear patterns within the data. Compared to traditional models used in previous research, such as multinomial logistic regression, decision trees, or support vector machines, this approach shows a greater ability to anticipate seasonal trends and unexpected changes in crime risk. This is particularly valuable for monthly aggregated data at the county level, as socioeconomic factors influence criminal behavior. A key difference from earlier studies is the use of a multidimensional dataset that includes socioeconomic, demographic, and environmental indicators. This allows for a more comprehensive modeling of crime risk forecasting. Unlike many existing studies that only consider raw crime data or historical frequencies, this research takes a systemic view, providing deeper insight into the causal relationships and interdependencies among the factors. Additionally, normalizing crime rates per capita and using a balanced five-level risk classification improve the strength and practical relevance of the findings. Another innovative feature is the thorough comparison between the ARIMA-ANN model and standard classification techniques, using a wide range of evaluation metrics, including accuracy, precision, F1-score, and AUC-ROC. The results clearly show that the hybrid model excels in identifying complex temporal patterns and lowering misclassification rates in high-risk categories. This is important for developing proactive public safety strategies. This research pushes forward the field of crime analysis by providing not just a high-performing model, but also a repeatable, scalable, and flexible methodological framework. By effectively combining machine learning techniques with time series analysis, this study sets a new standard for applied research in social risk forecasting and serves as a valuable tool for guiding strategic decision-making in public safety and social policy areas.