## Course description

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.

## Midterm project

• You are welcome to browse previous midterm projects from 2016, 2018 and 2020

## Using the Great Lakes cluster

• 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: stats531w21_class.

• 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 hpc-support@umich.edu.