Time Series (Bologna) STATS4072

  • Academic Session: 2023-24
  • School: School of Mathematics and Statistics
  • Credits: 12
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No

Short Description

The course focuses on the fundamental theory of time series analysis.





Requirements of Entry

This course is only available to students on the Double Degree programme in Statistics with the University of Bologna.

Excluded Courses

Statistics 3T: Time Series [STATS3018]

Time Series [STATS4037]

Time Series (Level M) [STATS5030]




End-of-course examination, carried out in accordance with the assessment procedures and regulations of the University of Bologna.

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

This course aims 

■ to help students develop a thorough understanding of the theory of times series, both in the time and in the frequency domain

■ to explain how a time series can be decomposed into a trend and seasonal component;

■ to train students in performing parametric inference in time-series models; and

■ to train students in choosing an appropriate time series model.

Intended Learning Outcomes of Course

By the end of the course students will be able to:

■ analyze a time series in the time and in the frequency domain;

■ identify the stochastic process that has generated a time series based on the autocorrelation structure;

■ estimate and make inference on the parameters of a linear model for a stationary time series;

■ estimate time series components such as trend and seasonality by means of non-parametric and parametric methods;

■ recognise the most important models for time series data.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.