| CODE | SOR2230 | ||||||||||||||||
| TITLE | Time Series 1 | ||||||||||||||||
| UM LEVEL | 02 - Years 2, 3 in Modular Undergraduate Course | ||||||||||||||||
| MQF LEVEL | 5 | ||||||||||||||||
| ECTS CREDITS | 4 | ||||||||||||||||
| DEPARTMENT | Statistics and Operations Research | ||||||||||||||||
| DESCRIPTION | Description and examples of various types of time series; usage of statistical software for graphical display and preliminary analysis. - Smoothing and Detrending of times series: filtering, differencing, smooth-curve fitting, multiplicative and additive models, transformations; - Autocorrelation and Stationarity; - Probability Models for Time Series: White Noise, AR, MA, ARMA, ARIMA, SARIMA, Unit root processes; - Forecasting; - Tests for Randomness; - Goodness of Fit Diagnostics; - Multivariate Time Series Modelling with particular reference to bivariate processes. Study-Unit Aims: Statistics students are introduced to the analysis of time series data. The nature of such data is discussed with an emphasis of the differences between time series and independent data. The techniques studied are implemented in practice through various software and open source code routines (particularly in R) of one's choice. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - demonstrate a comprehensive grasp of the theoretical foundations underlying the time series techniques covered: - and also the types of applications which motivate these methods.be able recognise the types of real world applications that motivate the use of these methods; - cultivate a lifelong learning mindset of staying updated on advancements in time series analysis methodologies and their applications. 2. Skills By the end of the study-unit the student will be able to: - apply time series techniques to diverse fields and industries; - evaluate and select appropriate time series models based on the characteristics of the data;. - effectively interpret and communicate the results obtained from time series analyses; - demonstrate a critical awareness of the implications and limitations of time series models in practical scenarios; - use software and open source routines that can come in useful to implement such routines. Main Text/s and any supplementary readings: - Chatfield C., (1980), The Analysis of Time Series: An Introduction, Chapman and Hall - Hamilton J.D., (1994) Time Series Analysis, Princeton University Press - Brockwell P.J., Davis R.A. and Rockwell P.J., (2002) Introduction to Time Series and Forecasting, Springer - Brockwell P.J. and Davis R.A., (1996) Time Series: Theory and Methods, Springer - Tsay R.S., (2002) Analysis of Financial Time Series, John Wiley & Sons. Inc. - Cowpretwait, P.,(2009). Introductory Time Series with R. |
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| ADDITIONAL NOTES | Pre-Requisite Study-Units: SOR1220 (for students who started the course before academic year 2023/2024), SOR1222 (for students who started the course from academic year 2023/2024 onwards) | ||||||||||||||||
| STUDY-UNIT TYPE | Lecture and Practical | ||||||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Monique Borg Inguanez |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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