# STATS 326 : Applied Time Series Analysis

## Science

### Course Prescription

Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.

### Course Overview

Time series data arise in various areas, such as agriculture, crime, demography, health, meteorology, economics, and sales, among others. The analysis of these observed data at different time points leads to unique problems in statistical modelling and inference. This course provides a basic understanding of time series visualization, decomposition, regression, exponential smoothing methods, (seasonal) ARIMA models, dynamic regression models, model selection, and validation. Students get the opportunity to enhance their analytical and computer skills with exercises using R.

### Course Requirements

Prerequisite: 15 points from ECON 211, ENGSCI 314, STATS 201, 208 Restriction: STATS 727

### Capabilities Developed in this Course

 Capability 3: Knowledge and Practice Capability 4: Critical Thinking Capability 5: Solution Seeking Capability 6: Communication

### Learning Outcomes

By the end of this course, students will be able to:
1. Use appropriate data visualizations to identify features present in time series. (Capability 3, 4 and 5)
2. Identify the most appropriate time series models for a given problem. (Capability 3, 4 and 5)
3. Fit commonly used time series regression models, exponential smoothing methods, (seasonal) ARIMA models, X13, and dynamic regression models using R, and make forecasts using these models. (Capability 3, 4 and 5)
4. Interpret and communicate the software output for a given time series model. (Capability 3, 4, 5 and 6)
5. Perform model selection and cross-validation. (Capability 3, 4 and 5)

### Assessments

Assessment Type Percentage Classification
Assignments 20% Individual Coursework
Quizzes 10% Individual Coursework
Test 20% Individual Test
Final Exam 50% Individual Examination
1 2 3 4 5
Assignments
Quizzes
Test
Final Exam

### Tuākana

Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at
https://www.auckland.ac.nz/en/science/study-with-us/pacific-in-our-faculty.html
https://www.auckland.ac.nz/en/science/study-with-us/maori-in-our-faculty.html

### Special Requirements

The mid-semester test will be held on campus in person. The date and time will be advised on Canvas at the beginning of the semester.

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, a typical weekly workload includes:

• 2 hours of lectures
• A 1-hour tutorial
• 7 hours of reviewing the course content and working on assignments and/or test preparation

### Delivery Mode

#### Campus Experience

Attendance is expected at scheduled activities including labs to complete components of the course.
Lectures will be in-person and on-campus, and recordings will be made available after lectures. Other learning activities including labs will not be available as recordings.
The course will not include live online events including group discussions.
Attendance in person will be required for any on campus test and exam
The activities for the course are scheduled as a standard weekly timetable.

### Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

• Forecasting: Principles and practice, by R. H. Hyndman and G. Athanasopoulos
• Time series analysis and its applications: With R examples, by R. H. Shumway and D. S. Stoffer
• Introduction to time series and forecasting, by P. J. Brockwell and R. A. Davis
• Forecasting with exponential smoothing: The state space approach, by R. J. Hyndman, A. B. Koehler, J. K. Ord and R. D. Snyder

### Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

No changes required.

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

### Class Representatives

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

### Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

### Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page .

This should be done as soon as possible and no later than seven days after the affected test or exam date.

### Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

### Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter .

### Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 06/11/2023 08:40 a.m.