STAT 578

Fall 2016

Part of Term 1
Aug 22-Dec 7
Topics in Statistics
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Credit: 4 hours.

May be repeated if topics vary. Prerequisite: Consent of instructor.

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CRN Type Section Time Day Location Instructor Section Details
30959
Lecture-Discussion
A1
11:00AM -12:20PM
TR
Engineering Hall
Guerrier, S
Part of Term:
1
Date Range:
08/22/16-12/07/16
Section Title:
Time Series Forecasting
Section Info:
Time Series Forecasting: The goal of this course is to provide a foundation for modeling time dependent data through a project-based exploration of real world data sets. Projects will focus on obtaining predictions for short-term and long-term problems such as estimating temperatures, portfolio optimization, website visits, and so on. The course will address classical time series methods such as seasonal autoregressive integrated moving averages, generalized autoregressive conditional heteroskedasticity (GARCH), and their use within regression before looking at more recently developed techniques such as vector autoregression moving averages, Multivariate GARCH, State-Space Models, Threshold Models, and Machine Learning techniques. Prerequisites: STAT 410 and basic knowledge of one programming language (preferably R).
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
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