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MA50085: Time series

[Page last updated: 23 October 2023]

Academic Year: 2023/24
Owning Department/School: Department of Mathematical Sciences
Credits: 6 [equivalent to 12 CATS credits]
Notional Study Hours: 120
Level: Masters UG & PG (FHEQ level 7)
Period:
Semester 2
Assessment Summary: CW 25%, EX 75%
Assessment Detail:
  • Coursework (CW 25%)
  • Examination (EX 75%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites:
Learning Outcomes: At the end of the course, the student should be able to:
* compute and interpret a correlogram and a sample spectrum
* derive the properties of ARIMA and state-space models
* choose an appropriate ARIMA model for a given set of data and fit the model using R
* compute forecasts for a variety of linear methods and models.
* demonstrate critical thinking and a deep understanding of some aspects of time series theory and application.


Aims: To introduce a variety of statistical models for time series, cover the main methods for analysis and give practical experience in fitting such models.

Skills: Numeracy T/F A
Problem Solving T/F A
Written and Spoken Communication F A

Content: Introduction: Examples, simple descriptive techniques, trend, seasonality, the correlogram.
Probability models for time series: Stationarity; moving average (MA), autoregressive (AR), ARMA and ARIMA models.
Estimating the autocorrelation function and fitting ARIMA models.
Forecasting: Exponential smoothing, Forecasting from ARIMA models.
Stationary processes in the frequency domain: The spectral density function, the periodogram, spectral analysis.
State-space models: Dynamic linear models and the Kalman filter.

Course availability:

MA50085 is Optional on the following courses:

Department of Mathematical Sciences

Notes:

  • This unit catalogue is applicable for the 2023/24 academic year only. Students continuing their studies into 2024/25 and beyond should not assume that this unit will be available in future years in the format displayed here for 2023/24.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
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