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Unit of study_
STAT3925: Time Series (Advanced)
2025 unit information
This unit will study the basic concepts and methods of time series analysis and forecasting which are applicable in many real-world problems in numerous fields including economics, finance, insurance, physics, ecology, chemistry, computer science and engineering. The first part of this unit will study the basic methods of modelling and analysing time series data (ie. data containing serially dependent structure). This is achieved through learning standard time series procedures on identification of components, autocorrelations, partial autocorrelations and their sampling properties. After setting up these basics, students will learn the theory of stationary univariate time series models including AR, MA, ARMA and ARIMA and their properties. Then the identification, estimation, diagnostic checking, decision making, and forecasting methods from these models will be developed with applications. The second part of this unit of study will consider the spectral theory of stationary time series, estimation of spectra using periodogram and consistent estimation of spectra using lag-windows. Further, the methods of analysing long memory time series through ARFIMA, heteroscedastic time series models including ARCH, GARCH and other volatility models from financial econometrics with applications will be studied. Finally, the theory of cross-correlations, the modelling and analysis of vector ARMA (VARMA) and vector ARIMA (VARIMA) will be studied with applications. Throughout this unit of study, a statistical package will be used to demonstrate various simulations, modelling, forecasting and applications. By completing this unit of study, students will develop essential basis for further studies towards a higher degree in statistics, financial econometrics or financial time series. In addition, the skills gain through this unit of study will form a strong foundation to work in any related area in financial industry or in a suitable research organization.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
|---|---|
| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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STAT2X11 and (MATH1062 or MATH1962 or MATH1972 or MATH1X03 or MATH1907 or MATH1X23 or MATH1933) |
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Corequisites:
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None |
| Prohibitions:
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STAT4025 |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. 1.Explain and examine time series data and identify components of a time series; remove trends, seasonal and other components.
- LO2. Identify stationarity time series; sample autocorrelations and partial autocorrelations, probability models for stationary time series.
- LO3. Explain homogeneous nonstationary time series, simple and integrated models and related results.
- LO4. Apply estimation and fitting methods for ARIMA models via MM and MLE methods.
- LO5. Apply hypothesis testing, diagnostic checking and goodness-of-fit tests methodology.
- LO6. Construct forecasting methods for ARIMA models.
- LO7. Explain spectral methods in time series analysis.
- LO8. Apply financial time series and related models to straightforward problems.
- LO9. Apply the methods of analysis of GARCH and other models for volatility.
- LO10. Explain and apply methods of vector time series models
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 2 2025
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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