Course Title: Time Series and Forecasting

Part A: Course Overview

Course Title: Time Series and Forecasting

Credit Points: 12.00

Terms

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

MATH2204

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2019,
Sem 2 2021,
Sem 1 2022,
Sem 1 2023,
Sem 1 2024

Course Coordinator: Dr Haydar Demirhan

Course Coordinator Phone: +61 3 9925 2729

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

Course Coordinator Location: 15.04.15

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

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

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

Students are assumed to have knowledge of fundamental statistical concepts, hypothesis testing and R programming when participating in this course.


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 BP350 Bachelor of Science:

  • PLO 1 Apply a broad and coherent knowledge of scientific theories, principles, concepts and practice in one or more scientific disciplines. 
  • PLO 2 Analyse and critically examine scientific evidence using methods, technical skills, tools and emerging technologies in a range of scientific activities.
  • PLO 3 Analyse and apply principles of scientific inquiry and critical evaluation to address real-world scientific challenges and inform evidence based decision making.
  • PLO 4 Communicate, report and reflect on scientific findings, to diverse audiences utilising a variety of formats employing integrity and culturally safe practices.


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 blended online and face to face 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.


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: Assignments (2)
Weighting 50%
This assessment task supports CLOs 1, 2, 3, 4 and 5

Assessment Task 2: Project
Weighting 50%
This assessment task supports CLOs 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.