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MA50085: Time series

[Page last updated: 03 August 2022]

Academic Year: 2022/23
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)
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)
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.

Programme availability:

MA50085 is Optional on the following programmes:

Department of Mathematical Sciences


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