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

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

Pre-requisites:

MATH1324 Applied Analytics

MATH1318 Time Series Analysis or equivalent.

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.


Course Learning Outcomes (CLOs):

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

There will be a combination of lectorials to cover theoretical concepts and practical lab sessions to apply theory to practice and analytics using the R package

Learning activities of the course include lectorials and practice sessions where you will apply the methodology covered in the lectures. Lectorials and pre-recorded videos 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.

The course material is designed to offer you a balance between R commands and examples and practical examples via the lectorials. In addition the problem sessions labs will provide students opportunities to apply R to prescribed tasks using R.

Exponential smoothing approaches, regression methods for time series data, state space models and model selection procedures will be covered in the lectorials. You will apply contents of each week to analyse real data using R software. Assessment will be through a mixture of assessment tasks, and a project.



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 the Canvas shell and course website as well.

All course materials, including lecture notes,  practical exercises, datasets, and assignments will be posted on Canvas 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: 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.