Regime Switch Analysis on Time-series Data for Financial Prediction

In recent years, the emergence of massive temporal data has become a reality in almost all aspects of social life, economic activity, security and defense, and poses a big challenge for existing methods. This project focuses on prediction from temporal data that arise ubiquitously in healthcare, social, industrial and financial fields. Events typically include changes in health status such as hospital readmission or death, evolution in social networks such that communities arise or vanish, modifications in energy consumption (e.g., wattage changes) and regime changes in stock markets. To address these, we need advanced survival analysis methods and algorithms.

Intern: 
Philippe Chatigny
Faculty Supervisor: 
Shengrui Wang
Province: 
Quebec
Partner University: 
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