Time Series Analysis has wide applicability in economic and financial fields but also to geophysics, oceanography, atmospheric science, astronomy, engineering, and many other fields of practice. This course will illustrate time series analysis using many applications from these fields.
By the end of this class, students will:
- Learn the widely used time series models such as univariate ARMA/ARIMA modelling, (G)ARCH modeling, and VAR model.
- Be given fundamental grounding in the use of some widely used tools, but much of the energy of the course is focus on individual investigation and learning.
This class is more about the opportunity for individual discovery than it is about mastering a fixed set of techniques.
This course does not count toward any specific specialization, and does not count as a foundational course.
Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.
Before Taking This Class...
Suggested Background Knowledge
A sound familiarity with undergraduate or graduate statistics and probability but also basic programming proficiency, linear algebra, and basic calculus. A sound familiarity with linear regression modeling.
Technical Requirements and Software
Throughout this course, students will be exposed to not only fundamental concepts of time series analysis but also many data examples using the R statistical software. Thus by the end of this course, students will also familiarize with the implementation of time series models using the R statistical software along with interpretation for the results derived from such implementations.
All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.