2.0

credits

Average Course Rating

Students learn advanced statistical models and state-of-the-art machine learning methods for modeling and analyzing time series data with applications in business, economics, and finance. Time series analysis is a methodology to exploit historical data generated by real-world systems to forecast the future values of these systems. This course will balance theory and practical applications of time series modeling and analysis at a level accessible to a wide variety of students and practitioners in business, economics, finance, engineering, and quantitative social sciences. Emphasis is placed on the development and choice of appropriate models, how to estimate and test model parameters, and forecast future values for making better business decisions. Furthermore, challenges in dealing with big time series data problems are discussed and recent advances in overcoming their practical issues are presented. Each lecture consists of three components including theory, case studies, and tools, to train students with the underlying theory of time series models, real-world applications, and Python programming practices. Moreover, students will experience the whole process of predictive analytics in practice through a final group project, by finding appropriate real-world time series data, modeling and analyzing the data, making predictions, and providing managerial decisions. The final outcomes will be showcased through poster presentations.