Course Title: Forecasting

Part A: Course Overview

Course Title: Forecasting

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH1307

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2009,
Sem 2 2011,
Sem 1 2013,
Sem 2 2014,
Sem 1 2016

MATH1307

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 2 2017,
Sem 2 2019,
Sem 2 2021,
Sem 2 2023

Course Coordinator: Irene Hudson

Course Coordinator Phone: +61 3 9925 3224

Course Coordinator Email: irene.hudson@rmit.edu.au

Course Coordinator Location: 15.03.19


Pre-requisite Courses and Assumed Knowledge and Capabilities

 

Required Prior Study

You should have satisfactorily completed following course/s before you commence this course.

  • MATH1324 Applied Analytics (Course ID 012021)
  • MATH1318 Time Series Analysis (Course ID 012016)

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.

 

Assumed Knowledge

Knowledge of R software


Course Description

This course provides and introduction to methods in forecasting data. Techniques in univariate forecasting using exponential smoothing, time series regression models, state space models, and model selection are covered. The emphasis is on methods and the analysis of time series data using the covered approaches with the R statistical software.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Communication

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.


On completion of this course you should be able to:

  1. Analyse past patterns in time series data and develop appropriate models for forecasting;
  2. Estimate forecasting models using R software
  3. Apply methods of forecasting to practical problems including assessing forecast accuracy and criticising the results to enhance forecasting performance;
  4. Report and interpret analytic results based on real-world data concisely and accurately
  5. Prepare oral and written reports on a forecasting model and its accuracy in terms that would allow a non-expert to make business plans and decisions


Overview of Learning Activities

 

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both. 

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course


Overview of Learning Resources

 

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Assessment Tasks:

Assessment Task 1: Assignment 1
Weighting 30%
This assessment task supports CLOs 1, 2, 3, 4 and 5

Assessment Task 2: Assignment 2
Weighting 35%
This assessment task supports CLO 1, 2 and 3

Assessment Task 3: Project
Weighting 35%
This assessment task supports CLO 1, 2, 3, 4 and 5

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.