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  2. Time Series Forecasting [MAT2440]

School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

  • Unit Title

    Time Series Forecasting

  • Unit Code

    MAT2440

  • Year

    2025

  • Enrolment Period

    1

  • Version

    2

  • Credit Points

    15

  • Full Year Unit

     N

  • Mode of Delivery

    On Campus
    Online

  • Unit Coordinator

    Dr Eben AFRIFA-YAMOAH

Description

This unit provides a comprehensive introduction to time series analysis and forecasting techniques essential for modeling temporal data. Students will explore fundamental concepts including time series decomposition, exponential smoothing methods, regression approaches, and Box-Jenkins methodology. The unit emphasises practical application through hands-on analysis, where students learn to identify appropriate models, implement robust estimation procedures, conduct thorough diagnostic testing, and generate reliable forecasts. Special attention is given to intervention analysis for assessing the impact of events on time series patterns. Through real-world case studies and applications, students develop skills in model selection, validation, and forecasting that are valuable across various fields including data science, finance, economics, health, environmental science, and business analytics.

Prerequisite Rule

Students must pass 1 unit from BES1100, MAT1114

Equivalent Rule

Unit was previously coded MAT2215

Learning Outcomes

On completion of this unit students should be able to:

  1. Identify and fit appropriate models to a variety of time series data using statistical software.
  2. Apply time series models for forecasting and prediction.
  3. Carry out a diagnostic check for a fitted time series model.
  4. Explain the details of a number of time series forecasting models.
  5. Identify ethical and cultural issues related to gathering and storing data.

Unit Content

  1. Applications of the time series forecasting models.
  2. Autoregressive, moving average, combined ARMA and Box-Jenkins time series forecasting models.
  3. Diagnostic checks for the suitability of a given time series forecasting model to a particular application.
  4. Exponential smoothing methods.
  5. Intervention analysis
  6. Time series decomposition.
  7. Time series regression.

Learning Experience

Students will attend on campus classes as well as engage in learning activities through ECU's LMS

JoondalupMount LawleySouth West (Bunbury)
Semester 113 x 2 hour lectureNot OfferedNot Offered
Semester 113 x 2 hour tutorialNot OfferedNot Offered

For more information see the Semester Timetable

Additional Learning Experience Information

The unit is delivered in 5E mode (Engage, Explore, Explain, Elaborate, Evaluate) that promotes student autonomy through self-paced learning materials complemented by interactive tutorials and computer workshops. This structure enables students to develop practical skills and theoretical understanding while receiving guided support.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

ON CAMPUS
TypeDescriptionValue
PortfolioTime series analysis portfolio50%
ProjectProject report30%
PresentationVideo presentation of project outcomes20%
ONLINE
TypeDescriptionValue
PortfolioTime series analysis portfolio50%
ProjectProject report30%
PresentationVideo presentation of the project outcomes20%

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Assessment

Students please note: The marks and grades received by students on assessments may be subject to further moderation. Informal vivas may be conducted as part of an assessment task, where staff require further information to confirm the learning outcomes have been met. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.

Academic Integrity

Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work as well as any generative artificial intelligence tools that may have been used. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.

Breaches of academic integrity can include:

  • Plagiarism: Copying the words, ideas or creative works of other people or generative artificial intelligence tools, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).
  • Unauthorised collaboration (collusion): Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).
  • Contract cheating: Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.
  • Cheating in an exam: Using or having access to unauthorised materials in an exam or test.

Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.

ECU's policies and more information about academic integrity can be found on the student academic integrity website.

All commencing ECU students are required to complete the Academic Integrity Module.

Assessment Extension

In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000001386.

Special Consideration

Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000003318.

MAT2440|2|1

School: Science

This unit information may be updated and amended immediately prior to semester. To ensure you have the correct outline, please check it again at the beginning of semester.

  • Unit Title

    Time Series Forecasting

  • Unit Code

    MAT2440

  • Year

    2025

  • Enrolment Period

    2

  • Version

    2

  • Credit Points

    15

  • Full Year Unit

     N

  • Mode of Delivery

    On Campus
    Online

  • Unit Coordinator

    Dr Eben AFRIFA-YAMOAH

Description

This unit provides a comprehensive introduction to time series analysis and forecasting techniques essential for modeling temporal data. Students will explore fundamental concepts including time series decomposition, exponential smoothing methods, regression approaches, and Box-Jenkins methodology. The unit emphasises practical application through hands-on analysis, where students learn to identify appropriate models, implement robust estimation procedures, conduct thorough diagnostic testing, and generate reliable forecasts. Special attention is given to intervention analysis for assessing the impact of events on time series patterns. Through real-world case studies and applications, students develop skills in model selection, validation, and forecasting that are valuable across various fields including data science, finance, economics, health, environmental science, and business analytics.

Prerequisite Rule

Students must pass 1 unit from BES1100, MAT1114

Equivalent Rule

Unit was previously coded MAT2215

Learning Outcomes

On completion of this unit students should be able to:

  1. Identify and fit appropriate models to a variety of time series data using statistical software.
  2. Apply time series models for forecasting and prediction.
  3. Carry out a diagnostic check for a fitted time series model.
  4. Explain the details of a number of time series forecasting models.
  5. Identify ethical and cultural issues related to gathering and storing data.

Unit Content

  1. Applications of the time series forecasting models.
  2. Autoregressive, moving average, combined ARMA and Box-Jenkins time series forecasting models.
  3. Diagnostic checks for the suitability of a given time series forecasting model to a particular application.
  4. Exponential smoothing methods.
  5. Intervention analysis
  6. Time series decomposition.
  7. Time series regression.

Learning Experience

Students will attend on campus classes as well as engage in learning activities through ECU's LMS

JoondalupMount LawleySouth West (Bunbury)
Semester 113 x 2 hour lectureNot OfferedNot Offered
Semester 113 x 2 hour tutorialNot OfferedNot Offered

For more information see the Semester Timetable

Additional Learning Experience Information

The unit is delivered in 5E mode (Engage, Explore, Explain, Elaborate, Evaluate) that promotes student autonomy through self-paced learning materials complemented by interactive tutorials and computer workshops. This structure enables students to develop practical skills and theoretical understanding while receiving guided support.

Assessment

GS1 GRADING SCHEMA 1 Used for standard coursework units

ON CAMPUS
TypeDescriptionValue
PortfolioTime series analysis portfolio50%
ProjectProject report30%
PresentationVideo presentation of project outcomes20%
ONLINE
TypeDescriptionValue
PortfolioTime series analysis portfolio50%
ProjectProject report30%
PresentationVideo presentation of the project outcomes20%

Disability Standards for Education (Commonwealth 2005)

For the purposes of considering a request for Reasonable Adjustments under the Disability Standards for Education (Commonwealth 2005), inherent requirements for this subject are articulated in the Unit Description, Learning Outcomes and Assessment Requirements of this entry. The University is dedicated to provide support to those with special requirements. Further details on the support for students with disabilities or medical conditions can be found at the Access and Inclusion website.

Assessment

Students please note: The marks and grades received by students on assessments may be subject to further moderation. Informal vivas may be conducted as part of an assessment task, where staff require further information to confirm the learning outcomes have been met. All marks and grades are to be considered provisional until endorsed by the relevant School Progression Panel.

Academic Integrity

Integrity is a core value at Edith Cowan University, and it is expected that ECU students complete their assessment tasks honestly and with acknowledgement of other people's work as well as any generative artificial intelligence tools that may have been used. This means that assessment tasks must be completed individually (unless it is an authorised group assessment task) and any sources used must be referenced.

Breaches of academic integrity can include:

  • Plagiarism: Copying the words, ideas or creative works of other people or generative artificial intelligence tools, without referencing in accordance with stated University requirements. Students need to seek approval from the Unit Coordinator within the first week of study if they intend to use some of their previous work in an assessment task (self-plagiarism).
  • Unauthorised collaboration (collusion): Working with other students and submitting the same or substantially similar work or portions of work when an individual submission was required. This includes students knowingly providing others with copies of their own work to use in the same or similar assessment task(s).
  • Contract cheating: Organising a friend, a family member, another student or an external person or organisation (e.g. through an online website) to complete or substantially edit or refine part or all of an assessment task(s) on their behalf.
  • Cheating in an exam: Using or having access to unauthorised materials in an exam or test.

Serious outcomes may be imposed if a student is found to have committed one of these breaches, up to and including expulsion from the University for repeated or serious acts.

ECU's policies and more information about academic integrity can be found on the student academic integrity website.

All commencing ECU students are required to complete the Academic Integrity Module.

Assessment Extension

In some circumstances, Students may apply to their Unit Coordinator to extend the due date of their Assessment Task(s) in accordance with ECU's Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000001386.

Special Consideration

Students may apply for Special Consideration in respect of a final unit grade, where their achievement was affected by Exceptional Circumstances as set out in the Assessment, Examination and Moderation Procedures - for more information visit https://askus2.ecu.edu.au/s/article/000003318.

MAT2440|2|2