DATA4400 Fitting and evaluating time series models Assignment
Assessment 2 Information
| Subject Code: | DATA4400 |
| Subject Name: | Data-driven Forecasting |
| Assessment Title: | Fitting and evaluating time series models |
| Assessment Type: | Individual report(20%); Quiz (10%) |
| Word Count: | Report = 500 Words(+/-50%) |
| Weighting: | 30% |
| TotalMarks: | 30 |
| Submission: | Individualreport:
Quiz via portal |
| Due Date: | Report on Tuesday at 11.55 pm AEST Week 9 Quiz by Friday 5 pm AEST of Week 9 |
Your Tasks
- Part A: Submit an Individual report on Forecasting Techniques and Time Series
- Part B: Quiz (30 minutes): Answer multiple choice questions based on Workshops 1 to 8 material.
Assessment Instructions
Part A Report: Forecasting for Decision-Making: A Data-Driven Approach
Scenario:
You are a Data Scientist at a global analytics firm and have been assigned to forecast trends in a unique timeseries of monthly values (each student will receive different data). Your forecasts will help business leaders optimise decision-making in areas such as demand planning, climate predictions, financial projections, or consumer trends.
Your task is to apply a series of forecasting techniques and produce a report explaining your methodology, results, and recommendations.
You will be provided with a dataset on Monday of Week 7.
Your report should be well-structured, professionally formatted, and include clear visualisations to communicate findings effectively.
You can use software of your choice such as Excel, Exploratory, Orange, Python, or Tableau Public to assist.
Important: If using Tableau Public, you must include screenshots of all your parameters, model evaluation metrics, and settings changes as you will not be able to download the project from Tableau Public to submit the file via the portal.
1. Exponential Smoothing & Holt-Winters Method (3 marks)
- Apply Simple Exponential Smoothing and Holt-Winters (Triple Exponential Smoothing) and forecast for one year ahead. Calculate the MASE for Holt-Winters over the final year, where you define a monthly seasonal naive forecast as the value in the latest corresponding month.
- Interpret all of the analysis above.
2. Prophet and correlogram (5 marks)
- Fit Prophet to the time series with additive seasonals and with multiplicative seasonals. Calculate the RMSE. Continue with the option that gives the lower RMSE.
- Identify seasonality patterns and trends and discuss their impact on forecasting decisions.
- Obtain the Remainder series from your Prophet fit. Calculate the ACF and PACF of the Remainder series. Explain the role of ACF and PACF plots in time series forecasting.
- Interpret all of the analysis above.
3. Seasonal ARIMA Modelling (4 marks)
- Implement seasonal ARIMA models.
- Discuss the model selection criteria you use to choose a suitable model. State the order of your chosen model together with its RMSE
- Forecast for 1 year ahead and provide 68% limits of prediction.
- Compare your chosen seasonal ARIMA results to Holt-Winters and state which model you would recommend in this context.
4. Var models (6 marks)
- What is a VAR model, and when is it used in forecasting?
- Find a real-world application of VAR and discuss how VAR can be implemented.
- Explain the concept of Granger causality and its application in time series forecasting.
- Is there evidence of Granger causality in the VAR application you found?
5. Professionalism (2 Marks)
- Your report should be clear, well-structured, and formatted professionally to effectively communicate your forecasting insights. Include graphs, tables, and statistical outputs to support your discussion. Ensure clear labelling and professional formatting.
- Your referencing must be correct and without the use of Generative AI in the report.
- Your report must be submitted in Word format to the correct portal on time.
REPORT SUBMISSION GUIDELINES
Technical Tools Allowed:
- Excel, Tableau Public, Exploratory, Orange, Python (or any relevant software). If using Tableau Public, you must include screenshots of all your parameters, model evaluation metrics, and
settings changes as you will not be able to download the project from Tableau Public to submit the file via the portal.
File Format:
- Submit as a Word document.
- Submit other software files.
Part B Quiz:
- The quiz will be available from Monday 10:00 am - Friday 5 pm (AEST) Week 9.
- You will enter the “Attempt Quiz” on MyKBS in the Assessment Table for A2.
- Answer 15 multiple-choice questions.
- Once you start the quiz you will have 30 minutes to complete it.
- You can start the quiz anytime between 10 am AEST Monday to 5 pm AEST Friday, Week 9. If you miss this window, the quiz will not be opened to you again.
- The quiz can only be attempted once.
- Backtracking of questions is not allowed. You must complete the question before moving on to the next one. You will not be able to go back to the previous question.
- This is an open-book quiz however the use of Generative AI is not permitted.
Generative AI Traffic Lights
Please see the level of Generative AI that this assessment has been designed to accept:
| Traffic Light | Amount of Generative Artificial Intelligence (GenerativeAI) usage |
Evidence Required | This assessment (✓) |
Level 1 | Prohibited:
No GenerativeAI allowed
This assessment showcases your individual knowledge, skills and/or personal experiences in the absence of Generative AI support. |
The use of generative AI is prohibited for this assessment and may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. |
✓ |
Level 2 |
Optional:
You may use GenerativeAI for research and content generation that is appropriately referenced.
See assessment instructions for details
This assessment allows you to engage with Generative AI as a means of expanding your understanding, creativity, and idea generation in the research phase of your assessment and to produce content that enhances your assessment. I.e., images. You do not have to use it. |
The use of GenAI is optional for this assessment.
Your collaboration with GenerativeAI must be clearly referenced just as you would reference any other resource type used.Click on the link below to learn how to reference GenerativeAI.
https://library.kaplan.edu.au/referencing-other- sources/referencing-other-sources-generative-ai
In addition, you must include an appendix that documents your GenerativeAI collaboration including all prompts and responses usedfor the assessment.
Unapproved useof generative AI as per assessment details during the content generation parts of your assessment may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. Ensure you follow the specific assessment instructions in the section above. | |
Level 3 |
Compulsory:
You must use GenerativeAI to complete your assessment
See assessment instruction for details
This assessment fully integrates Generative AI, allowing you to harness the technology's full potential in collaboration with your own expertise.
Always check your assessment instructions carefully as there may still be limitations on whatconstitutes acceptable use, and these may be specific to each assessment. |
You willbe taught how touse generative AI and assessed on its use.
Your collaboration with GenerativeAI must be clearly referenced just as you would reference any other resourcetype used. Click on the link belowto learn how to reference GenerativeAI.
https://library.kaplan.edu.au/referencing-other- sources/referencing-other-sources-generative-ai
In addition, you must include an appendix that documents your GenerativeAI collaboration including all prompts and responses usedfor the assessment.
Unapproved useof generative AI as per assessment details during the content generation parts of your assessment may potentially result in penalties for academic misconduct, including but not limited to a mark of zero for the assessment. Ensure you follow the specific assessment instructions in the section above. |
Marking Rubric
Part A Report: Forecasting for Decision-Making: A Data-Driven Approach - Total Marks: 20
| Criteria | Fail (0-49%) | Pass (50-64%) | Credit (65-74%) | Distinction(75-84%) | High Distinction (85- 100%) |
1. Exponential Smoothing & Holt-Winters Method (3 Marks) | 0 – 1.4 marks Incorrect or incomplete application of Simple Exponential Smoothing and Holt- Winters. No or incorrect RMSE calculation. Poor or missing interpretation of results. | 1.5 – 1.8 marks Basic application of smoothing methods but lacks clarity or contains errors. RMSE is calculated but not well explained. Limited interpretation of results. | 1.9 – 2.1 marks Good implementation of smoothing models with correctRMSE calculation. Some interpretation is present but lacks depth. | 2.2 – 2.5 marks Strong implementation with correct forecasting techniques and well- explained RMSE calculation. Insightful interpretation of results. | 2.6 – 3 marks Exceptional execution with clear application of smoothing techniques, well- supported RMSE calculations, and deep, insightful analysis of forecasting outcomes. |
| 2. Correlogram (5 Marks) | 0 – 2.4 marks Incorrect or missing Prophet model application. No meaningful identification of seasonality or trends. No or incorrect ACF/PACF calculation. No interpretation provided. | 2.5 – 3.1 marks Basic Prophetmodel applied with some seasonality/trend identification but lacks clarity. ACF/PACF is calculated but not well explained. Limited interpretation of findings. | 3.2 – 3.6 marks Good Prophet implementation with correct seasonality and trend identification. ACF/PACF results are present with some interpretation. | 3.7 – 4.2 marks Strong Prophet model analysis with well-supported seasonality/trend findings. ACF/PACF analysis is clear and correctly interpreted. | 4.3 – 5 marks Exceptional analysis, demonstrating deep understanding of seasonality, trends, ACF/PACF, and their impact on forecasting. Clear, well-supported interpretations. |
| 3. Seasonal ARIMA Modelling (4 Marks) | 0 – 1.9 marks Incorrect or missing seasonal ARIMA implementation. No discussion of model selection. No forecast or incorrect prediction intervals. No comparison to Holt-Winters. | 2 – 2.5 marks Basic seasonal ARIMA model applied with minimal discussion of model selection. Forecast provided but lacks explanation. Limited comparison to Holt-Winters. | 2.6 – 2.9 marks Good seasonal ARIMA implementation with reasonable model selection criteria. Forecast provided with 68% prediction limits. Some comparison to Holt- | 3 – 3.4 marks Strong ARIMA model implementation with well-justified model selection. Forecasting results are well-explained and meaningfully compared to Holt- Winters. | 3.5 – 4 marks Exceptional ARIMA analysis with insightful model selection justification, well- structured forecasting, and a thorough comparison to Holt- Winters. Clear |
Winters but lacks depth. | recommendation based on evidence. | ||||
| 4. VAR Models (6 | 0 – 2.9 marks | 3 – 3.8 marks | 3.9 – 4.4 marks | 4.5 – 5.2 marks | 5.3 – 6 marks |
| Marks) | Noor incorrect | Basic explanation of | Goodexplanation of | Strong explanation of | Exceptional |
| explanation of VAR. | VARwith limited real- | VARwith a | VARwith a well- | understanding of VAR | |
| Noreal-world | world application. | reasonable real- | supported real-world | andGranger causality | |
| application | Granger causality is | world example. | application. Granger | witha compelling, | |
| discussed. No | mentioned but not | Granger causality is | causality is clearly | well-researched real- | |
| mention of Granger | well-explained. Real- | discussed with some | defined and linked to | world example. | |
| causality or incorrect | world application is | relevance to the | thereal-world | Strong, insightful | |
| explanation. No real- | weakor unclear. | scenario. | example. | discussion on | |
| world connection. | causality relationships. | ||||
| 5. | 0 – 0.9 marks | 1 – 1.2 marks | 1.3 – 1.4 marks | 1.5 – 1.7 marks | 1.8 – 2 marks |
| Professionalism | Poorly structured | Basic report structure | Well-structured | Strongly structured | Exceptional report |
| (2 Marks) | report. Formatting | withsome formatting | report with | andprofessional | withhighly |
| issues, missing | inconsistencies. Some | appropriate | report with clear, well- | professional | |
| graphs or tables, and | graphs and tables are | formatting. Graphs | labelled visual | presentation. Visually | |
| lackof clear | included but not well- | andtables are | elements. Correct | appealing and well- | |
| labelling. Incorrect | labelled. Referencing | present and mostly | referencing and well- | organized with | |
| referencing or | ispresent but may | well-labelled. | formatted | correctly referenced | |
| missing citations. | contain minor issues. | Correct referencing | submission. | sources and perfect | |
| Submitted late or in | Submitted on time. | withminor errors. | formatting. Submitted | ||
| thewrong format. | ontime. |
Part B Quiz – 10 marks
Quiz
Select correctmultiple-choice answers. |
10 |
Computerised marking |
Note 1: A penalty will be awarded if any part of the assessment is deemed by the assessor where it shows an over-reliance on AI-generated content in your answer. There needs to be a demonstration of original thought.
If you want to challenge the penalty awarded based on Note 1, your assessment will be submitted to Academic Integrity for a second opinion and further investigations.
Important Study Information
Academic Integrity Policy
KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.
What is academic integrity and misconduct? What are the penalties for academic misconduct? What are the late penalties?
How can I appeal my grade?
Click here for answers to these questions: http://www.kbs.edu.au/current-students/student-policies/.
Word Limits for Written Assessments
Submissions that exceed the word limit by more than 10% will cease to be marked from the point at which that limit is exceeded.
Study Assistance
Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on the MyKBS Academic Success Centre page. Click here for this information.
Late assignment submission penalties
Penalties will be imposed on late assignment submissions in accordance with Kaplan Business School’s Assessment Policy.
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*Assignments submitted at any stage within the first 24 hours after deadline will be considered to be one day late and therefore subject to the associated penalty.
If you are unable to complete this assessment by the due date/time, please refer to the Special Consideration Application Form, which is available at the end of the KBS Assessment Policy:
https://www.kbs.edu.au/wp- content/uploads/2016/07/KBS_FORM_AssessmentPolicy_MAR2018_FA.pdf