This course gives an introduction to time series analysis using time domain methods and frequency domain methods. The goal is to acquire the theoretical and computational skills required to investigate data collected as a time series. The first half of the course will develop classical time series methodology, including auto-regressive moving average (ARMA) models, regression with ARMA errors, and estimation of the spectral density. The second half of the course will focus on state space model techniques for fitting structured dynamic models to time series data. We will progress from fitting linear, Gaussian dynamic models to fitting nonlinear models for which Monte Carlo methods are required. Examples will be drawn from ecology, economics, epidemiology, finance and elsewhere.
A course outline, course information and grading policies are described in the syllabus.
There are further POMP case studies, in a similar style, on Ebola modeling, measles transmission, and dynamic variation in the rate of human sexual contacts.
Please read the grading policy in the syllabus before submitting homework.
Homework 0. Setting up your computational environment. Nothing to submit.
There is no assigned homework for the last two weeks of the semester. You should work on your final project. The lectures for these weeks contain material that will be useful for your final projects. Also, you can optionally look at the exercises in the notes for Chapter 15 which have worked solutions online.
You're welcome to browse previous final projects for 2016, 2018 and 2020 . If building on previous source code, note that there are some differences between versions of the software package pomp. The pomp version 2 upgrade guide can be helpful. The changes from pomp 2.0 to the current pomp 3.3 are smaller.
Introductory notes for using our class account on the greatlakes cluster. This is optional but may be helpful for your final project.
If you are already familiar with using R on Great Lakes, all you need to know is the class account:
You are expected to use our class account only for computations related to STATS/DATASCI 531.
Please share knowledge about cluster computing between group members, and/or on piazza, to help everyone who wants to learn these skills.
Cluster-related questions can also be emailed to firstname.lastname@example.org.