Time Series Analysis and Stochastic Processes
|Title||Time Series Analysis and Stochastic Processes (58020)|
|Instructor||Andrew Siegel (firstname.lastname@example.org)|
Stochastic processes are driven by random events. They can be used to model phenomena in a broad range of disciplines, including science/engineering (e.g. computational physics, chemistry, and biology), busi- ness/finance (e.g. investment models and operations research), and computer systems (e.g. client/server workloads and resilience modeling). In many cases relatively simple stochastic simulations can provide estimates for problems that are difficult or impossible to model with closed-form equations.
In this class we focus on the rudimentary ideas and techniques that underlie stochastic time series analysis, discrete events modeling, and Monte Carlo simulations. Course lectures will focus on the basic principles of probability theory, their efficient implementation on modern computers, and examples of their application to real world problems. Upon completion of the course, students should have an adequate background to quickly learn in depth specific Monte Carlo approaches in their chosen field of interest.
Coursework4 homework assignments (50%), 6 short quizzes (20%), two exams (30%).
Courses: Required: Immersion programming or waiver. Recommended: Immersion math, basic back- ground in probability.
Langagues: Matlab will be used for course examples. Matlab, Julia, IDL, or Python are recommended for assignments. Any language is acceptable as long as you do not use high-level libraries to replace programming exercises.
Core Programming, Recommended: Immersion Math or passing score on math placement exam.
Non-MPCS students need to complete a course request form.