Forecasting and Time Series Methods
This is a course in the analysis of time series data with emphasis on appropriate choice of models for estimation, testing, and forecasting. Topics or methodologies covered may include Univariate Box-Jenkins for fitting and forecasting time series; ARIMA models, stationarity and nonstationarity; diagnosing time series models; transformations; forecasting: point and interval forecasts; seasonal time series models; modeling volatility with ARCH, GARCH; modeling time series with trends; and other methods. The R Shiny App development will be covered to help students obtain skills of making prototype of their models and ideas.
Contributors:
Dr. Yichen Qin, Associate Professor of Business Analytics at University of Cincinnati
Dr. Martin Levy, Professor of Business Analytics at University of Cincinnati