Predictive Analytics/Forecasting

Econometrics This course will expose students to the most popular forecasting techniques used in industry. We will cover time series data manipulation and feature creation, including working with transactional and hierarchical time series data as well as methods of evaluating forecasting models. We will cover basic univariate Smoothing and Decomposition methods of forecasting including Moving Averages, ARIMA, Holt-Winters, Unobserved Components Models and various filtering methods (Hodrick-Prescott, Kalman Filter). Time permitting, we will also extend our models to multivariate modeling options such as Vector Autoregressive Models (VAR). We will also discuss forecasting with hierarchical data and the unique challenges that hierarchical reconciliation creates. The course will use the R programming language though no prior experience with R is required.

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OrganizationHow well the course was organized

4.45 / 5

ChallengeHow intellectually challenging students found it

4.41 / 5

AttendanceHow necessary attendance was

3.94 / 5

AssignmentsHow helpful assignments were

4.56 / 5