ANLY 565 - Time Series and Forecasting

(3 semester hours)
Prerequisites: None
Description: This course covers key analytical techniques used in the analysis and forecasting of time series data. Specific topics include the role of forecasting in organizations, exponential smoothing methods, 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, transfer function modeling, intervention analysis, and multivariate time series analysis techniques such as Vector Autoregression (VAR), Cointegration and Vector Error Correction Model (VECM).