This course is an introduction to time series analysis. Course topics include linear regression model and its diagnostics, central concepts of weakly stationary processes, ARIMA-models and their properties, stationarity of ARIMA-models, forecasting with ARIMA-models, Kalman filter, and introduction to dynamic regression models. Software R is used in the exercises of the course.
After passing the course the students can analyse and forecast time series using regression models and ARIMA-models. Students are able to apply linear regression model to analyse and forecast dependent variable under the model assumptions. In addition, the students are able to conduct diagnostic tests to validate the model assumptions. Students are familiar with the concept weakly stationary processes and they understand the most important related concepts including the autocorrelation function, partial autocorrelation function, and the spectral function. Students are also able to apply these functions in analysign real time series, for example, in recognizing seasonal fluctuations. After taking the course, the students know ARIMA-models and their key properties. In addition, the students are able to model and predict the future behaviour of observed time series using ARIMA-models. The students also know basic concepts of dynamic regression models.
Language of Instruction: English, All the lectures, exercises, exams etc are in English only!
Workload: Lectures 24h (2), Exercises 24h (2), Homework assignments 48 h, reading and studying the lecture materials 36 h
Assessment Methods and Criteria: Homework assignments, exercise points, exam
Study Material: Lecture slides
Substitutes for Courses: Mat-2.3128 Prediction and Time Series Analysis
Prerequisites: MS-A05XX First course in probability and statistics, MS-A02XX Differential and integral calculus 2, and MS-A000X Matrix algebra
Lecturer: Pauliina Ilmonen, pauliina.ilmonen(a)aalto.fi
Lectures are an important part of this course. Attendance is not compulsory, but highly recommended. If you are unable to attend the lectures, you are expected to ask for notes from the other students.
Exercises are another very important part of this course. Attendance is not compulsory, but again highly recommended. You do get points by attending the exercises and by doing your homework assignments. Trying your very best is enough! Your solutions do not have to be correct. The exercise points are valid until the end of June 2020.
There are several exercise groups. Please attend one of the theory exercise groups and one of the computer exercise groups.
Students should be present and ready with their computers turned on at the beginning of the computer exercise session. Arriving late is not allowed.
How to pass this course?
You are expected to
Attend the lectures and be active - not compulsory, no points, but highly recommended.
Participate to exercises and solve your homework problems - not compulsory, but highly recommended - max 6 points.
Take the exam - max 30 points.
Max total points = 30 + 6 = 36: You need at least 18 points (or 15 points from the exam) in order to pass the course.
How to get a good grade?
Attend the lectures and be active!
Work hard on your homework assignments.
Be active in the exercises!
Study for the exam!
Grading is based on the total points as follows: at least 18p (or 15p from the exam) -> 1, at least 20p -> 2, at least 22p -> 3, at least 26p -> 4, at least 28p -> 5. Please note that it is very difficult to get grade 5 without attending the exercises. The reason for this is that learning to use R, and learning to conduct statistical analysis in practice, are crucial parts of the course.
In the exam, the focus is on the lecture material and on the lecture discussions. Note that on top of the weekly lecture slides, you should also study the self-study Logistic regression -lecture slides for the exam.
In the exam you may have your pens and pencils, a ruler and an eraser. On top of that you may have one A4 of notes. The rules for the note are: size A4, text on one side only, it must be hand-written, your name has to be on the top right corner of the note. Other materials, such as formulae books or calculators, you may not have in the exam. Please take an id-card with you to the exam. The exam organizers do not know you.