• Assignment 1 

• Assignment 2 

Quiz 1 will be in class on February 5th (1 hour and 20 minutes). Please bring your student ID to the exam. The quiz will test all of the material up to and including Section 4 in the course notes. It will consist of long answer questions. You will be allowed one double-sided sheet of notes in the exam.

Practice problems for the quiz can be found here. Solutions to the practice problems will not be provided; please come talk with me in office hours if you want to work through any of the problems.

I recommend that you do the practice problems, review the homework questions, relevant sections in the textbook, and your notes in preparation for the quiz. 

• Week 1:  Stochastic processes, time series, classical decomposition into trend + seasonal + noise components, strict and weak stationarity, autocovariance and autocorrelation functions and their properties, white noise, random walks.  

• Week 2: Computing the ACVF for an MA(q) and the AR(1) process, bias and variance of the sample mean, sample autocovariance function, asymptotic distribution of the sample autocorrelation of white noise, beginning of discussion on OLS regression for a mean function with trends or periodicity. 

• Week 3: More on OLS and GLS regression, smoothing time series via a window smoother and kernel smoothers, the backshift operator, differencing, seasonal differencing,  revisiting the MA(q) process in terms of backshifts, discussion on the identifiability of MA processes.  

• Introduction R Code  

• Smoothing and Differencing R Code 

• Simulating AR and MA R Code 

• Simulating ARMA Models and Making Predictions 

• 
  

• The Frequency Domain: Estimating and Examining Periodograms and Spectral Densities 

Course Description: Stationary series, spectral analysis, models in time series: autoregressive, moving average, ARMA and ARIMA. Smoothing series, computational techniques and computer packages for time series.

Prerequisites: STAT 372 and 378. 

Grading:

Grade breakdown

• 5 assignments for 35% of the total grade. The lowest assignment grade is dropped.

• 2 quizzes, each worth 15%.

• The final exam is worth 35%.

Assignments: All assignments are to be submitted on Canvas. You may scan handwritten solutions or write up solutions in LaTeX (preferred). If you choose to write up your solutions by hand please make sure that they are legible. For coding questions please submit relevant code chunks and output as part of your solution, while also including your raw code in a separate file. Assignments are meant to be completed individually without the assistance from your peers or generative AI models.

Late policy: 25% is subtracted from the grade of a given assignment for every day that this assignment is late. Assignments are due at 11:59 PM MST on the day indicated in the syllabus. 

Resources:

Textbooks:

There is no required textbook for this course. However, we will loosely be following 

Time Series Analysis and its Applications by Shumway and Stoffer (2009). This book can be downloaded here.

Two other books that may be useful are:

Introduction to Time Series and Forecasting by Brockwell and Davis (2016). This book can be downloaded here.

Time Series: Theory and Methods by Brockwell and Davis (1991). This book can be downloaded here. 

The former book is another introductory book on time series. The latter book is more advanced, but is a classic.

Software: We will be using R throughout this course. Coding portions of assignments should be done in R. 

Other resources: Professor Adam Kashlak taught a previous version of this course. His lecture recordings and course notes are bound to be helpful and can be found here and here respectively. The material covered in this version of the course will be similar, but not identical, to the material covered by Professor Kashlak. 

Class Time, Office Hours, and Contact Information:

Class time: Tuesdays and Thursdays, 9:30-10:50 AM, GSB 8-59.

Office hours: Tuesdays and Thursdays, 10:50-11:50 AM, CAB 475. 

My email is: mccorma2[AT]ualberta[DOT]ca