Course Title: Forecasting

Part A: Course Overview

Course Title: Forecasting

Credit Points: 12.00


Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2204

City Campus

Undergraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

Sem 2 2010,
Sem 2 2011,
Sem 2 2012,
Sem 2 2013,
Sem 2 2014,
Sem 2 2015

Course Coordinator: Haydar Demirhan

Course Coordinator Phone: +61 3 9925 2729

Course Coordinator Email: haydar.demirhan@rmit.edu.au

Course Coordinator Location: 8.9.83

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1324 Introduction to Statistics or equivalent.

 

Assumed knowledge of R Software


Course Description

This course provides you with a working knowledge of forecasting methods. Techniques in univariate forecasting using exponential smoothing, regression methods for time series data, stationary and non-stationary time series models for seasonal and non-seasonal time series data and model selection procedures are covered. The emphasis is on methods and the analysis of time series data using the R statistical software.

Please note that if you take this course for a bachelor honours program, your overall mark in this course will be one of the course marks that will be used to calculate the weighted average mark (WAM) that will determine your award level. (This applies to students who commence enrolment in a bachelor or honours program from 1 January 2016 onwards. See the WAM information web page for more information.)


Objectives/Learning Outcomes/Capability Development

 

This course contributes to the following Program Learning Outcomes for BH119 Bachelor of Analytics (Hons) and BP083 Bachelor of Science (Mathematics):

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. Demonstrate practical experience by having been exposed to problems based on real data
  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

Learning activities of the course include lectures and practice sessions where you will apply the methodology covered in the lectures. Face-to-face class time will be divided into two parts. In the first part, methodological aspects of contents will be illustrated with facilitated demonstrations, and then, students will apply the methodology over the real datasets and discuss analysis results to foster their understanding. Students will work in small groups or pairs in the practice sessions.

Exponential smoothing approaches, regression methods for time series data, seasonal and non-seasonal ARIMA models and model selection procedures will be covered in the lectures. You will apply contents of each week to analyse real data using R software. Assessment will be through a mixture of assignments, a project and exams.

 

Total study hours:

You will undertake 4 hours per week of face-to-face learning through lecture/lab sessions. Meanwhile it is recommended that an average of 6 hours/week of independent study is expected.


Overview of Learning Resources

A prescribed textbook will be used throughout the semester. A list of recommended textbooks for this course will also be provided on Blackboard and course website as well.

All course materials, including lecture notes, practical exercises, datasets, and assignments will be posted on Blackboard LMS and will also be available over a separate course website.

 

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats


Overview of Assessment

Assessment Tasks:

 

Assessment Task1: Assignments

Weighting 20%

This assessment task supports CLOs 1, 2, 3, 4, and 5

 

Assessment Task 2: Mid-Semester test

Weighting 20%

This assessment task supports CLO 1, 2, and 3

Assessment Task 3: Project

Weighting 20%

This assessment task supports CLO 1, 2, 3, 4, and 5.

Assessment 4: Final Exam

Weighting 40% 

This assessment supports CLOs 1, 2, 3, and 4