## 1 Introduction
There is a clear movement in predictive analytics towards hybrid models that bring together the strengths of both statistical and machine learning methods, aiming for better accuracy and more reliable results [10]. This study examines Galati County in Romania, a region that has changed a lot in the last eleven years. There have been shifts in society, the economy, migration, and patterns of criminal behavior. Using a detailed dataset from January 2014 to December 2024 [11; 12], \"Crime risk\" was defined as a multiclass variable based on the crime counts adjusted by population size. Class balancing corrects for historical imbalances that could otherwise skew model performance and impact policy [13; 14]. This study systematically compares the ARIMA-ANN hybrid model with several established multiclass classification methods, using a variety of evaluation metrics and visualization tools. All steps in the data science process are thoroughly documented to ensure transparency and reproducibility. The criteria for feature selection, the approach to constructing the target variable, and the application of domain knowledge at each stage are clearly explained. The findings provide practical insights for public safety practitionersin Romania and demonstrate a methodological approach that can be adapted to similar contexts elsewhere. The paper is structured as follows: Section 1 reviews past research on hybrid crime forecasting and multiclass classification in criminology. Section 2 describes our methods, data processing steps, and model design. Section 3 presents the results, showing both graphs and tables for performance comparison. In Section 4, we discuss the results, while Section 5 draws conclusions and looks at what our results mean for policy and future studies. Over the last twenty years, research in crime prediction has grown fast. Law enforcement agencies and policymakers now use data-based approaches to select where to place prevention resources, manage operations, and build long-term safety plans [15]. Early work in this area used statistical models like ARIMA, which could spot patterns over time and help set up prediction frameworks based on real evidence [16]. However, since ARIMA assumes all patterns are linear, it misses sudden shocks, structural changes, and the fact that crime data often fail to remain stable over time [8]. This limitation has led to the rise of hybrid models. A large body of work now focuses on hybrid ARIMA-ANN models. These can learn complex relationships and pick up on how outside factors relate to crime [17; 18]. The original hybrid ARIMA-ANN approach, first suggested by [PERSON] [10], uses ARIMA to model linear trends and then sends the leftover error (residuals) to an ANN, which can handle the nonlinear aspects. This two-step method has proven to improve predictions in real cases, from regional crime to city-level demand or even disease forecasting. Many studies confirm that hybrid models are good at catching sudden surges in crime during social or economic crises, or when big policy changes happen [1]. Beyond hybrid time series models, advances in machine learning and classification have given us better tools to break down crime risk. Methods like multinomial logistic regression, decision trees, random forests, and support vector machines (SVMs) can classify crime risk into multiple categories using a range of indicators--social, economic, demographic, or environmental [19; 20]. Adding specific variables, such as unemployment rate, economic health, city infrastructure, or weather, makes these models more generalizable and supports more detailed risk assessments [21; 22]. Yet, criminological data often suffer from class imbalance. To fix this, careful processing steps need to be applied, such as resampling or designing balanced target variables [23; 24; 25]. Recent comparison studies find that even though tree-based models and SVMs often perform well, hybrid ARIMA-ANN models can do better, especially when the data have time dependencies, nonlinear trends, or sudden changes [26].