Subject details

Students who successfully complete this subject will be able to:

  1. Plot time series and describe their characteristics
  2. Demonstrate fundamental concepts in time series modelling, such as time series decomposition and stationarity
  3. Compute indices based on time series data
  4. Appraise the limitations of regression as a forecasting tool
  5. Demonstrate univariate forecasting methods such as ARIMA and exponential smoothing and identifying appropriate models for a project
  6. Assess forecast performance using key measures of forecast accuracy such as MSE and MAPE
  7. Interpret forecast results for a general non-statistical audience.
    • Time series forecasting methods
    • Measures of accuracy
    • Introduction to smoothing methods
    • Methods for stationary forecasting
    • Methods for trend forecasting
    • Seasonal forecasts by smoothing
    • Decomposition methods
    • Regression and forecasting with cycles
    • Removal of non-stationarity
    • Interpretation of the autocorrelation function and the partial autocorrelation function
    • ARIMA model identification
    • SARIMA model identification
  • Study resources

    • Instructional methods

      • Discussion forum/Discussion Board
      • Online Quizzes/Tests
      • Online assignment submission
      • Web links
    • Print materials

      • Welcome letter
    • Online materials

      • Printable format materials

Equivalent subjects

You cannot enrol in this subject if you have successfully completed any of the following subject(s) because they are considered academically equivalent:

  • SWI-HMS782

You must have successfully completed the following subject(s) before starting this subject:

SWI-STA60005-Statistical Practice 2 , or SWI-HMS771

Special requirements

  • OtherDetails -
    • Other special requirement

This subject introduces students to a range of forecasting methods and their application to planning and decision-making. It teaches teach students about common tools and packages used in forecasting; the use of historical data to identify appropriate forecasting model(s); the use of accuracy measures to check the adequacy of the selected model; and the use the final model to forecast future values.

Please note: assessment values are indicative only, details will be advised at the start of the subject.

  • Quizzes — Online (10%)
  • Assignments — 2 (40%)
  • Invigilated Exam (50%)

Textbook information is pending.

Related degrees