Times Series Analysis and Forecasting

1 Unit · Graduate

This course covers analytical methods for time series analysis and forecasting. Specific topics include the role of forecasting in organizations, exploratory data analysis, stationary and non-stationary time series, autocorrelation and partial autocorrelation functions, univariate autoregressive integrated moving average (ARIMA) models, seasonal models, Box-Jenkins methodology, regression models with ARIMA errors, multivariate time series analysis, and non-linear time series modeling including exponential smoothing methods, random forest analysis, and hidden Markov modeling. Recommended prior course: MSDS 410-DL . (Required: (1) MSDS 420-DL or CIS 417-DL and (2) MSDS 422-DL or CIS 435-DL .)