Title | Time Series Analysis and Forecasting |
Quarter | Summer 2017 (July 2017) |
Instructor | Dr Yuri Balasanov (yuri.balasanov@acads.org) |
Syllabus | Course Description This is a fast paced 12 week course in Time Series Analysis for individuals with background in Linear Algebra, Calculus, Statistics and Probability. The course will introduce concepts like autocorrelation, stationarity, cointegration and more. The course will use time series concepts for developing sophisticated predictive models for financial markets. This course will use R for all programming assignments. Course Contents
At the completion of the course, students will be able to do the following:
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Instruction Format | Coursework will have following four important components:
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Assessment | A letter grade A,B,C,D or F for the course will be decided based on
Projects: 40% of the final grade
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TextBook | Time Series Analysis by James D Hamilton |
Pre-Requisite | Prior experience with R, strong background in Statistics and Probability |
Time | Lecture Time: 8:00 pm – 9:30 pm EST, Tuesday evenings Lab Time: 8:00 pm – 9:30 pm EST, Sunday evenings Virtual Office Hour time:TBA |
Location | Online |
TA Information | TBA |
Effort Required | 6-10 hours per week |
Certification | Participants will receive an instructor-signed certificate with a Pass grade if they score 50% and will receive Pass with distinction above 80% |
Get certificate of course completion
Attend highly interactive and hands on TA sessions and get help on projects and in-class programming sessions
Get access to course material, projects, graded homework