DAMSL-211 Time series analysis
January 16, 2026 2026-01-16 11:01DAMSL-211 Time series analysis
Type
Elective
Course Code
DAMSL-211
Teaching Semester
B Semester
ECTS Credits
10
Student Performance Evaluation
Homework and/or Lab assignments, Final Exam and/or project
Prerequisite Courses
Calculus I, Linear Algebra I, Probabilities
Syllabus
The course presents predictive models for time series. The first part f the course presents Time Series Regression whereas the second part focuses on ARIMA
models. R software (the lingua franca of Statistics) is utilized throughout the course, in a wide variety of examples, based on real and simulated data.
- Linear Regression with One Predictor; Inference in Regression and Correlation
- Diagnostics and Remedial Measures; Simultaneous Inferences
- Matrix Approach to Linear Regression; Multiple Regression
- Models for Quantitative and Qualitative Predictors; Model Selection and Validation; Diagnostics
- Weighted Least Squares; Serial Correlation
- Exploratory Analysis of Time Series Data; Time Series Regression
- Stationarity
- ARMA models
- ARMA model Identification and Maximum Likelihood Estimation
- Forecasting with ARMA models
- ARIMA and seasonal models
- Multivariate Time Series
- Forecasting Experiments; Rolling-Window Estimation and Evaluation
Learning Outcomes
- Upon successful completion of this course students will develop an in-depth understanding of linear regression and ARIMA models.
- Students will have learned alternative ARIMA and time series regression model-building strategies, depending on the number of available predictors.
- Students will gain hands-on experience in classic (e.g. exploratory analyses, diagnostic checks) and modern topics (e.g. cross-validation, quantile regression, penalized estimation) by analyzing real (or simulated) datasets using statistical software.
