Master
2026/2027
Time Series Analysis
ID 1213961
Type:
Elective course (Master of Finance)
Delivered by:
HSE Banking Institute
Where:
HSE Banking Institute
When:
1 year, 3 module
Online hours:
20
Open to:
students of one campus
Language:
English
ECTS credits:
3
Contact hours:
8
Course Syllabus
Abstract
Time Series Analysis (Master level) is an elective course designed for the first year Master students of “Finantial Analytic” Program. This is an intermediate course of Time Series Theory for the students specializing in the field of Finance and Banking. The course is taught in English.The stress in the course is made on the sense of facts and methods of time series analysis. Conclusions and proofs are given for some basic formulas and models; this enables the students to understand the principles of economic theory. The main stress is made on the economic interpretation and applications of considered economic models.
Learning Objectives
- The students should get acquainted with the main concepts of Time Series theory and methods of analysis.
- Students should know how to use them in examining financial processes and should understand methods, ideas, results and conclusions that can be met in the majority of books and articles on economics and finance.
- Students should master traditional methods of Time Series analysis, intended mainly for working with time series data.
- Students should understand the differences between cross-sections and time series, and those specific economic problems, which occur while working with data of these types.
Expected Learning Outcomes
- Understand trend-seasonal decomposition
- Understand the ETS model and theta-model
- Know how to do Box-Cox transformation
- Build the ACF and PACF
- Interpret the ARIMA models
- Conduct stationarity tests
- Know how to create predictors
- Know the difference between the ARIMAX and ARDL model
- Learn how to compare models
- Learn how to handle missing data
- Know how to detect anomalies
- Learn about structural breaks
Course Contents
- Trend-seasonal decomposition and exponential smoothing models
- ARIMA models
- Time series forecasting
- Pre-procssing data
Assessment Elements
- Test 1There are two graded online tests. Exact dates and time slots will be published in the LMS after confirmation of the official Module 3 timetable. The tests assess conceptual understanding, interpretation of statistical output, recognition of methodological errors, and basic numerical reasoning. Questions may include multiple-choice items, numerical answers, short code fragments, diagnostics, or AI-generated outputs that students must evaluate critically.
- Test 2There are two graded online tests. Exact dates and time slots will be published in the LMS after confirmation of the official Module 3 timetable. The tests assess conceptual understanding, interpretation of statistical output, recognition of methodological errors, and basic numerical reasoning. Questions may include multiple-choice items, numerical answers, short code fragments, diagnostics, or AI-generated outputs that students must evaluate critically.
- Final ProjectThe final project is an individual applied forecasting exercise using a real economic or financial TS. The objective is to compare forecasting approaches based on predictive performance and to demonstrate that the complete analytical pipeline is methodologically valid, reproducible, and robust to key data and modeling choices.
Bibliography
Recommended Core Bibliography
- Banerjee, A., Dolado, J. J., Galbraith, J. W., & Hendry, D. (1993). Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data. Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.oxp.obooks.9780198288107
- Enders, W. (2015). Applied Econometric Time Series (Vol. Fourth edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639192
- Tsay, R. S. (2010). Analysis of Financial Time Series (Vol. 3rd ed). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=334288
Recommended Additional Bibliography
- Mills, T. C., & Markellos, R. N. (2008). The Econometric Modelling of Financial Time Series: Vol. 3rd ed. Cambridge University Press.