Schedule: 26.10.2015 - 03.12.2015
Teaching Period (valid 01.08.2018-31.07.2020):
II Autumn (2018-2019, 2019-2020)
Learning Outcomes (valid 01.08.2018-31.07.2020):
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 analysing real time series, for example, in recognising 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.
Content (valid 01.08.2018-31.07.2020):
The 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.
Assessment Methods and Criteria (valid 01.08.2018-31.07.2020):
Homework assignments, exercise points, exam
Workload (valid 01.08.2018-31.07.2020):
Lectures 24h (2), Exercises 24h (2), Homework assignments 48h, reading and studying the lecture materials 36h
Study Material (valid 01.08.2018-31.07.2020):
Lecture slides and the textbook Peter J. Brockwell, Richard A. Davis: Time Series – Theory and Methods, Springer 2009 (reprint of the 2nd edition 1991).
Substitutes for Courses (valid 01.08.2018-31.07.2020):
Mat-2.3128 Prediction and Time Series Analysis
Course Homepage (valid 01.08.2018-31.07.2020):
Prerequisites (valid 01.08.2018-31.07.2020):
MS-A05XX First course in probability and statistics, MS-A02XX Differential and integral calculus 2, and MS-A00XX Matrix algebra.
Grading Scale (valid 01.08.2018-31.07.2020):
- Teacher: Niko Lietzén