Course overview

This course is intended to give students an overview of the theory and practical aspects of fitting time series models to fisheries and environmental data. The course will cover topics ranging from autocorrelation and crosscorrelation, autoregressive (AR) and moving average (MA) models, univariate and multivariate state-space models, and estimating model parameters. This course also covers various aspects of assessing model performance and evaluating model diagnostics. The course is focused almost exclusively on problems and analyses in the time domain, and only briefly addresses methods for the frequency domain. In general, students will focus on conceptualizing analyses, implementing analyses, and making inference from the results.


Textbook

Holmes, E. E., M. D. Scheuerell, and E. J. Ward. Applied Time Series Analysis for Fisheries and Environmental data. eBook. Available here


Key dates

  • HW #1 Thurs Jan 21 11:59pm PST
  • HW #2 Thurs Jan 28 11:59pm PST
  • Project proposals due Fri Jan 29th 11:59pm PST
  • HW #3 Thurs Feb 4 11:59pm PST
  • HW #4 Thurs Feb 11 11:59pm PST
  • HW #5 Thurs Feb 18 11:59pm PST
  • HW #6 Thurs Feb 25 11:59pm PST
  • Final project due Fri Mar 12th 11:59pm PST
  • Peer reviews due Fri Mar 19th 11:59pm PST


Learning objectives

By the end of the quarter, students should be able to:

  • Understand the elements to classical decomposition

  • Understand how to use ACF and PACF to identify orders of ARMA(p,q) models for time series data

  • Apply appropriate diagnostic measures to identify any shortcomings in model assumptions

  • Understand how to use information theoretic methods and cross validation for model selection

  • Understand how to combine state and observation models into a state-space model

  • Use multivariate time series models with covariates to identify influential explanatory variables and do perturbation analyses

  • Use Dynamic Factor Analysis to identify common patterns among many time series

  • Use Dynamic Linear Models to allow for changing relationships between a response variable and any explanatory variable(s)

  • Prepare forecasts with uncertainty using time series models


Instructors

Eli Holmes
Research Fish Biologist, NOAA Fisheries
eeholmes@uw.edu

Eric Ward
Statistician, NOAA Fisheries
warde@uw.edu

Mark Scheuerell
Associate Professor, School of Aquatic & Fishery Sciences
scheuerl@uw.edu


Meeting times & locations

Lectures

Tuesday & Thursday from 1:30-2:50

Computer Lab

Thursday from 3:00-3:50

Office hours

By appointment


Pre-requisites

Students should have a working knowledge of the R computing software, such as that provided in FISH 552/553. Students should also have an understanding of basic probability and statistical inference.


Classroom conduct

We are dedicated to providing a welcoming and supportive learning environment for all students, regardless of their background, identity, physical appearance, or manner of communication. Any form of language or behavior used to exclude, intimidate, or cause discomfort will not be tolerated. This applies to all course participants (instructor, students, guests). In order to foster a positive and professional learning environment, we ask the following:

  • Please let us know if you have a name or set of preferred pronouns that you would like us to use

  • Please let us know if anyone in class says something that makes you feel uncomfortable[1]

In addition, we encourage the following kinds of behaviors:

  • Use welcoming and inclusive language

  • Show courtesy and respect towards others

  • Acknowledge different viewpoints and experiences

  • Gracefully accept constructive criticism

Although we strive to create and use inclusive materials in this course, there may be overt or covert biases in the course material due to the lens with which it was written. Your suggestions about how to improve the value of diversity in this course are encouraged and appreciated.

Please note: If you believe you have been a victim of an alleged violation of the Student Conduct Code or you are aware of an alleged violation of the Student Conduct Code, you have the right to report it to the University.


Access & accommodations

All students deserve access to the full range of learning experiences, and the University of Washington is committed to creating inclusive and accessible learning environments consistent with federal and state laws. If you feel like your performance in class is being impacted by your experiences outside of class, please talk with us.

Disabilities

If you have already established accommodations with Disability Resources for Students (DRS), please communicate your approved accommodations to us at your earliest convenience so we can discuss your needs in this course. If you have not yet established services through DRS, but have a temporary health condition or permanent disability that requires accommodations (e.g., mental health, learning, vision, hearing, physical impacts), you are welcome to contact DRS at 206-543-8924 or via email or their website. DRS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DRS.

Religious observances

Students who expect to miss class or assignments as a consequence of their religious observance will be provided with a reasonable accommodation to fulfill their academic responsibilities. Absence from class for religious reasons does not relieve students from responsibility for the course work required during the period of absence. It is the responsibility of the student to provide the instructor with advance notice of the dates of religious holidays on which they will be absent. Students who are absent will be offered an opportunity to make up the work, without penalty, within a reasonable time, as long as the student has made prior arrangements.


Technology

This course will revolve around hands-on computing exercises that demonstrate the topics of interest. Therefore, students are strongly recommended to bring their own laptop to class, although students are certainly free to work with one another. For students without access to a personal laptop: it is possible to check out UW laptops for an entire quarter (see the Student Services office for details).

All of the software we will be using is free and platform independent, meaning students may use macOS, Linux, or Windows operating systems. In addition to a web browser, we will be using the free R software and the desktop version of the R Studio integrated development environment (IDE). We will also be using various packages not contained in the base installation of R, but we will wait and install them at the necessary time. The instructor will be available during the first week of class to help students troubleshoot any software installation problems.

Students will also be required to have a user account on GitHub, which we will be using for file hosting and communications via “issues”. If you do not already have an account, you can sign up for a free one here. The instructor will provide training on how to use the intended features in GitHub.

Teaching methodology

This course will introduce new material primarily through prepared slides and hands-on demonstrations. Students will be expected to work both individually and collaboratively (to the extent possible given the current conditions); course content and evaluation will emphasize the communication of ideas and the ability to think critically more so than a specific pathway or method. Other areas of this website provide an overview of the topics to be covered, including links to weekly reading assignments, lecture materials, computer labs, and homework assignments.


Communication

This course will involve a lot of communication between and among students and the instructor. Short questions should be addressed to us via email; we will try my best to respond to your message within 24 hours. Under more normal circumstances, detailed questions would be addressed to us in person–either after class or during a scheduled meeting. In this case, however, we will schedule one-on-one or group video calls as needed.

In addition to email and video, we will use the “Issues” feature in GitHub to ask questions and assist others. Specifically, questions and answers can be posted to the issues in the course repository here.


Evaluation

Students will be evaluated on their knowledge of course content and their ability to communicate their understanding of the material via individual homework assignments (30%), a final project (40%), peer reviews (20%), and class participation (10%). There will be 6 homework assignments, each of which will count toward 5% of the final grade. Please note, all assignments must be turned in to achieve a passing grade.

Homework (30%)

Homework will be assigned each Thursday and is due by 11:59 PM PST on the following Thursday. It will consist of some short answers and R code based on topics covered in lab. There will be 6 assignments worth 5% each. Your learning in the class will be greatly enhanced by doing the homework which consist of applying the material you learn in each lecture to a data set.

Individual project (40%)

Each student will have to write a complete, publishable (<20 page) paper that may, or may not, serve as a component of their thesis/dissertation. Given that some students might not have their own data, students may also use data from the instructors, public datasets or datasets included in R libraries. See list of prior student projects for the types of projects done is prior years.

The techniques you will be learning would be appropriate for the EFI RCN NEON Ecological Forecast Challenge happening concurrently with the 2021 Fish 507 course. You are welcome to do one of the challenges as your individual project. You do not need to formally participate in the challenge (i.e. register as a team but you are welcome to do so). All the data are provided and the challenge lays out the goals (what to forecast) for each challenge. The Aquatic Ecosystems, Tick Abundance, and Beetle Abundance challenges would be appropriate for the class.

Peer reviews (20%)

Each student will have to provide 2 anonymous peer-reviews of their colleagues’ papers (10% each).

Participation (10%)

This is a graduate-level course and we expect a certain amount of engagement from each student, which includes attending and participating lectures and computer labs.

Students should discuss any potential schedule conflicts with the instructor during the first week of class.


Academic integrity

Faculty and students at the University of Washington are expected to maintain the highest standards of academic conduct, professional honesty, and personal integrity. Plagiarism, cheating, and other academic misconduct are serious violations of the Student Conduct Code. we have no reason to believe that anyone will violate the Student Conduct Code, but we will have no choice but to refer any suspected violation(s) to the College of the Environment for a Student Conduct Process hearing. Students who have been guilty of a violation will receive zero points for the assignment in question.


Mental health

We are in the midst of an historic pandemic that is creating a variety of challenges for everyone. If you should feel like you need some help, please consider the following resources available to students.

If you are experiencing a life-threatening emergency, please dial 911.

Crisis Clinic
Phone: 206-461-3222 or toll-free at 1-866-427-4747

UW Counseling Center
Phone: 206-543-1240
Immediate assistance

Let’s Talk

Hall Health Mental Health


Safety

If you feel unsafe or at-risk in any way while taking any course, contact SafeCampus (206-685-7233) anytime–no matter where you work or study–to anonymously discuss safety and well-being concerns for yourself or others. SafeCampus can provide individualized support, discuss short- and long-term solutions, and connect you with additional resources when requested. For a broader range of resources and assistance see the Husky Health & Well-Being website.


Food Pantry

No student should ever have to choose between buying food or textbooks. The UW Food Pantry helps mitigate the social and academic effects of campus food insecurity. They aim to lessen the financial burden of purchasing food by providing students access to shelf-stable groceries, seasonal fresh produce, and hygiene products at no cost. Students can expect to receive 4 to 5 days’ worth of supplemental food support when they visit the Pantry, located on the north side of Poplar Hall at the corner of NE 41st St and Brooklyn Ave NE. Visit the Any Hungry Husky website for additional information, including operating hours and additional food support resources.


Endnotes

[1] If the instructor should be the one to say or do something that makes a student uncomfortable, the student should feel free to contact the Director of the School of Aquatic and Fishery Sciences.


This site was last updated at 17:08 on 06 May 2021