STAT562 UNIVARIATE TIME SERIES ANALYSIS
| Course Code: | 2460562 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 7.0 |
| Department: | Statistics |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Prof.Dr. CEYLAN YOZGATLIGİL |
| Offered Semester: | Fall or Spring Semesters. |
Course Objectives
This course aims to (1) equip students with practical tools for empirical analysis of univariate time series data and (2) provide a solid introduction to the theoretical foundations of time series models. While traditional statistical methods often assume independence among observations, this course focuses on methods tailored to dependent data structures typical in time series. Emphasizing both theory and application, students will learn to analyze univariate time series using specialized software and develop the foundation necessary for further research in this field.
Course Content
Fundamental concepts in univariate time domain analyses, properties of autocovarience and autocorrelation of time series, stationary and nonstationary models, difference equations, autoregressive integrated moving average processes, model identification, parameter estimation, model selection, time series forecasting, seasonal time series models, testing for a unit root, intervention analysis, outlier detection, handling missing observations in time series, Fourier series, spectral theory of stationary processes and the estimation of the spectrum.
Course Learning Outcomes
By the end of the course, students will be able to:
- Understand key concepts of univariate time series and stochastic processes.
- Model and analyze stationary and non-stationary time series (AR, MA, ARMA, ARIMA).
- Apply forecasting techniques and update predictions.
- Identify and estimate model parameters using statistical methods.
- Perform diagnostic checks and model selection.
- Analyze seasonal, intervention, and outlier-affected time series.
- Use Fourier and spectral methods for frequency-domain analysis.
Program Outcomes Matrix
| Level of Contribution | |||||
| # | Program Outcomes | 0 | 1 | 2 | 3 |
| 1 | Ability for converting theoretical, methodological, and computational statistical knowledge into analytical solutions in researches requiring statistical analyses. | ✔ | |||
| 2 | Ability for specifiying problems in real life situations bearing uncertainty, forming hypotheses, modeling, application, and interpreting the results. | ✔ | |||
| 3 | Ability for using current technology, computer softwares for statistical applications, computer programming for specific problems when necessary, writing computer codes for speeding up statistical calculations, organizing and cleaning databases, and preparing them for statistical analyses, and data mining. | ✔ | |||
| 4 | Ability for taking part in intra/inter disciplinary team work, efficient use of time, taking responsibility as a team leader, and entrepreneurship. | ✔ | |||
| 5 | Ability for taking responsibility in solitary work and producing creative solutions. | ✔ | |||
| 6 | Ability for keeping up-to-date with current advancements in statistical sciences, doing research, being open-minded, and adopting critical thinking. | ✔ | |||
| 7 | Ability for effective communication both in Turkish and English in specification of statistical problems, analyes, and interpretation of findings. | ✔ | |||
| 8 | Ability for using the knowledge in the field of expertise for the welfare of the society. | ✔ | |||
| 9 | Ability for suggesting the researchers in a comprehensible way the appropriate statistical methods for problems in fields that use statistics such as economics, finance, industrial engineering, genetics, and medicine and apply if needed. | ✔ | |||
| 10 | Ability for catalyzing discussions and presentations, public speaking, making presentations, communicating topics of expertise to the audiance in a comprehensible way. | ✔ | |||
0: No Contribution 1: Little Contribution 2: Partial Contribution 3: Full Contribution