Master of Science (MSc)
Mathematics for Science and Finance
Faculty of Science
Guiding the thesis writing
Terrorism becomes more rampant in recent years because of separatism and extreme nationalism, which brings a serious threat to the national security of many countries in the world. The analysis of spatial and temporal patterns of terror data is significant in containing terrorism. This thesis focuses on building and applying a temporal point process called self-exciting point process to fit the terror data from 1970 to 2018 of 10 countries. The data come from the Global Terrorism database. Further, an application in predicting the number of terror events based on the self-exciting model is another main innovative idea, in which an algorithm combining simulation and machine learning methods for prediction is developed to achieve high accuracy for predicting the number of events in a year. In summary, the results in this thesis illustrate that the proposed self-exciting point process model can fit the trend of terror attacks for the majority of countries well and has potential to predict a short-time future pattern of the data.
Wang, Siyi, "SELF-EXCITING POINT PROCESS FOR MODELLING TERROR ATTACK DATA" (2021). Theses and Dissertations (Comprehensive). 2323.