Time series analysis for crime prediction: a case study of moving average approach

Currently, scientific and technological capabilities are advancing rapidly. Although these developments bring societal benefits, they have also accelerated the informatization and increasing sophistication of criminal activities. New forms of crime continuously emerge, posing significant threats to personal and property safety. To address this challenge, traditional investigative methods have become inadequate, necessitating the adoption of big data technologies by law enforcement agencies for crime prediction. In this study, the moving average approach was applied to forecast crime trends based on time series data. The prediction results were systematically optimized, aggregated, and analyzed. By integrating scientific methodologies at the foundational level, this research establishes a data-driven framework to support crime prevention, investigation, and suspect apprehension.