## 4 Discussion  
Comparing the hybrid ARIMA-ANN model with traditional multiclass classifiers using eleven years of monthly crime risk data from Galati County revealed several valuable insights. These findings are important not only for researchers but also for local officials seeking practical tools. First, when examining the time series for unemployment, net salary, tourist arrivals, and trade, a noticeable pattern emerges. Crime risk does not spike in isolation; it correlates with these economic and social indicators. Whenever job losses increase, wages decrease, or fewer tourists visit the county, crime risk rises. These relationships are visible in the results and align well with criminological theories that suggest that economic pressure makes communities more vulnerable. It is not just about people losing jobs; a drop in tourist numbers or external funding can create a tense local atmosphere, leading to more crime. Additionally, seasonal factors are apparent. During the warmer months and peak tourist seasons, crime risk also increases. This emphasizes that forecasting models should consider both regular cycles and external shocks, such as weather changes or shifts in tourism. The way the target variable was structured--dividing risk into five clear categories--proved to be a wise approach. Figure 3 shows that all five risk levels have a significant share of the data. This prevents the model from favoring one class and overlooking rare but crucial risk spikes. During significant events, such as the COVID-19 lockdown, immediate changes in these risk classes are noticeable. The distribution shifts quickly, reflecting real shocks, and the model remains responsive to these transitions. This ability to detect shifts in underlying patterns is a key advantage for real-world monitoring and prompt action. Looking at Figure 4, the autocorrelation and partial autocorrelation plots reveal that crime risk in one month is influenced by both preceding months and annual patterns. There is a mixture of short-term memory and longer cycles. The ARIMA part of the model effectively captures these recurring trends. For sudden changes or unusual jumps that do not fit a simple pattern, the ANN fills in the gaps. This division of tasks is significant; it enables the model to detect both obvious and subtle shifts in risk that many other models might miss. Table 2 shows that the hybrid model outperforms all metrics: accuracy, F1-score, and AUC-ROC. The advantage is substantial. While random forest and SVM models have their strengths, the hybrid model shows greater reliability in critical situations. It excels at identifying high and very high crime risk periods. Weaker models, like decision tree and logistic regression, fail to detect these risky windows effectively, which can lead to dangerous gaps in real situations. The confusion matrix in Figure 5 shows that most errors made by the hybrid model occur between adjacent classes, such as \"moderate\" and \"high.\" This is important, because mistaking a \"moderate\" crime risk month for a \"high\" one is less problematic than confusing a \"very low\" month with a \"very high.\" This characteristic makes the hybrid approach much safer for everyday use by police or local planners, ensuring resources are not misallocated significantly. The ROC curves in Figure 6 support this point of view. The hybrid model demonstrates a strong capacity to differentiate between varying levels of crime risk, particularly during periods characterized by elevated criminal activity. This capability is essential for issuing early warnings and preventing issues before they escalate. Traditional models struggle here, particularly when trying to identify rare, high crime risk months. The predictions from the hybrid model shown in Figure 7 closely track actual crime risk levels over recent years, even during turbulent periods like the pandemic or economic downturns. It is evident that the ARIMA-ANN model adjusts well to changes, not just repeating past trends but also recognizing new ones as they arise.