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Viewing: DNSC 6219 : Time Series Forecasting for Analytics

Last approved: Mon, 16 May 2016 08:49:10 GMT

Last edit: Tue, 05 Apr 2016 15:43:44 GMT

Catalog Pages referencing this course
Programs referencing this course
School of Business
Decision Sciences (DNSC)
DNSC
6219
Time Series Forecasting for Analytics
Time Series Forecasting
Fall 2016
3
Course Type
Lecture
Default Grading Method
Letter Grade
students in the master of science in business analytics degree program; program approval may be substituted
No
No

Corequisites

25
Refik Soyer
Professor of Decision Sciences and of Statistics
Funger Hall 415B
email: soyer@gwu.edu
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


Predictive analysis and blackbox models for time series and econometric forecasting; identifying hidden patterns and structures in the univariate and multivariate time series data and exploiting these for forecasting; use of SAS to apply different forecasting models and methodologies to real life time-series data.
(1) To identify univariate and multivariate models for real-life time series. (2) To validate the assumptions underlying these time series models. (3) To forecast from time series models and to evaluate their performance.

Course Attribute


Key: 10385