MFIN 6606 Forecasting Techniques (Fall: 3 )
The theory and practice of applied time series analysis will be explored. First the different segments (trend, seasonality, cyclical and irregular) of a time series will be analyzed by examining the Autocorrelation functions (ACF) and Partial Autocorrelation functions (PACF). The specifics model to model the various types of time series include linear regression, panel regression, seasonal decomposition, exponential smoothing, ARIMA modeling as well as combining models. This course is offered as an online hybrid course. In addition to the online lectures presented on the Canvas LMS, there are three required on-campus class meetings on Saturday morning Sept. 15th, Saturday morning Oct. 6th, and Saturday morning Dec. 1st. The midterm exam will also be held on campus on Monday evening Oct. 15th. Please see the course syllabus for additional details.
Prerequisites: Undergraduate prerequisite-ECON1151orECON1155 Statistics/Graduate prerequisite -OPER7725.
Cross listed with:
Last Updated: 10-Apr-18