Research on the method of predicting the trend of criminal activities based on time series analysis from the perspective of criminal procedure law

Criminal activity has always been a problem that seriously affects people’s sense of security and well-being in life, and it is also the focus of public security departments in terms of security prevention and control. This study aims to use scientific methods to analyze the time series of existing historical data related to criminal activities and to advance inference or determination of the future trend of criminal activities. In this paper, after mining the spatiotemporal pattern of criminal activities using the centered moving average method, the spatiotemporal cycle characteristics and time distribution characteristics of criminal activities are analyzed. Based on the conclusions drawn, an improved LSTNet algorithm is proposed by combining deep learning spatio-temporal network, convolutional neural network CNN, recurrent neural network RNN, autoregressive AR, and specially designed Skip-RNN, and a GRU-gated recurrent unit structure is used to realize the optimization of the prediction accuracy of the criminal activities, to solve the problems related to the spatio-temporal dependencies, and to establish an improved ST- Res Net crime prediction model. Finally, the prediction performance of the model is examined. The RMSE, MSE, and MAPE values of this paper’s prediction model are 0.88, 5.12, and 5.12%, respectively, which are better than other models. The experiments demonstrate that the prediction model presented in this paper can enhance the accuracy of criminal activity prediction to a certain extent and can be employed in the prevention and management of criminal activities.