The objective of this course is to introduce to students the methods of time series and forecasting in practice and the stochastic model building in time series and their use with applications. Some of the key mathematical results are stated without detailed proof to make the underlying theory accessible to a wider audience. Students need to have knowledge of introductory calculus, matrix algebra, and basic statistics and regression analysis. The emphasis is on methods and analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package (R-programs), provide the students with ample opportunity to develop skills in this area. The core of the course covers stationary processes, ARMA and ARIMA processes, forecasting methods, multivariate time series, vector auto-regression (VAR models), and an introduction to non-stationary time series.
Three lecture hours per week. 05-09-2024-05-12-2024 Lecture Tuesday, Thursday 11:30AM - 12:45PM, Robertson Library, Room 210
Three lecture hours per week. 05-09-2024-05-12-2024 Lecture Tuesday, Thursday 11:30AM - 12:45PM, Robertson Library, Room 210
- Teacher: Sami Khedhiri
Category: 2024F