Reference no: EM133923714

Data-driven Forecasting

Assessment - Forecasting Techniques and Time Series

Task
Develop a real-world forecasting project plan/proposal based on the learnings from the subject. This assessment is to be done individually.
Part A - Present an overview of your business project in class in Week 12
Part B - Students are to write a 1000-word report on Forecasting for a Business Problem

Assessment Description
This assessment seeks to simulate a real-world task that you may have to undertake in the future. Therefore, the assignment is non-prescriptive and requires you to pose a relevant, small, creative and significant forecasting problem to solve that could result in benefits to the organisation of choice.

In this assessment, you need to consider an organisation in an industry of your choice and articulate the steps this organisation needs to take to enable forecasting for data-driven decision making. You are required to source an example time series, or maybe more than one time series, to demonstrate expected forecasting outcomes.

The application does not need to be of major financial significance to the general community, for example, for one student it was the number of people viewing her YouTube channel. Another was a waste of fresh fruit and vegetables at a small grocery store. A small well-focused example is ideal. Get online assignment help-AI & plagiarism-free-now!

The analysis can be quite simple and performed in Excel, if appropriate. Aim for an interesting and original application of time series forecasting. You are not expected to compare different forecasting methods or to split time series into train and test segments.

Please consider Plan A first, if it is not feasible for you then choose either Plan B, or Plan C.

Plan A
Ideally, the application is based on an organisation you work for, or have worked for, in Australia or overseas. Use genuine time series data - though it can be scaled/perturbed if commercial confidentiality is an issue. A short time series of annual data may suffice. For example, a student was working for a not-for-profit organisation that runs care homes - annual data on several clients was read from annual reports from the past 8 years. Another student was working in a small grocery store. There was considerable wastage of fresh fruit and vegetables, but the store would not provide data. One approach would be to simulate realistic demand, based on your observations, and then investigate how a strategy based on Holt-Winters forecasts would reduce waste. Your facilitator can advise on the feasibility and suitability of using simulated time series for a specific application.

Plan B
Alternatively, you'll need to source some genuine up-to-date time series data and imagine some scenarios. For example, you might imagine you run a car dealership and need to order a mix of petrol and electric vehicles. Look for the time series of EV sales and consider the relationship with the number of charging facilities. Some interesting data sets can be found on GovHack - one of the 2024 challenges was to forecast tourist numbers for the Northern Territory. Other possibilities are public health issues including road traffic accidents. When you write up your application you can imagine you are working in a relevant organisation.

Plan C
Share prices are readily available and can be downloaded from Yahoo. Give some context. For example, imagine you are a stockbroker, and you have a client who wishes to invest in one of the four Australian clearing banks (ANZ, CBA, NAB, WESTPAC). Which one would out recommend for a one-year investment? You work for a company that offers an Employee Share Scheme - is this a good investment for you? You might investigate Granger causality between share prices, between a share price and volume traded, or between a share price and an economic indicator such as base rate, unemployment, building approvals etc. Remember that the VAR model used for assessing Granger causality is stationary - so work with log-returns of share prices.

Sample datasets maybe sourced from:
 an organisation if you work there,
 public repositories
 Open government data
 Open data such as share prices and commodity prices.

Part A - Individual Presentation
Duration: 3 to 8 minutes (as determined by the facilitator)

Slide 1-2: Company & Business Problem
Introduce the selected company and its industry.
Clearly define the business problem that requires forecasting.

Slide 3-4: Role of Forecasting
Explain how forecasting provides a solution to the identified problem.
Highlight expected benefits, such as improved decision-making or operational efficiency.

Slide 5-6: Data Overview
Present the dataset used, including key variables and units of measurement.
Mention the data source and its relevance to the problem.

Note: Keep visuals clear and concise. Focus on key takeaways and practical implications.

Part B - Forecasting Report (1000 words)
Organization Overview
Identify the organization and describe your familiarity with its business operations.
Explain its industry, key challenges, and decision-making processes relevant to forecasting.

Importance of Forecasting
Justify why forecasting is valuable for this organization.
Discuss how forecasting can enhance decision-making, efficiency, or financial performance.

Time Series Data
Describe the time series dataset used.
Provide the data source and details of key variables, including units of measurement, and other descriptive analysis.

Recommended Forecasting Technique
Propose an appropriate forecasting technique and justify your choice.
If using multiple time series, consider models such as VAR or Prophet with External Predictors.
Note: You are not required to fit multiple models for comparison but should focus on meaningful interpretation.

Results & Analysis
Present the results of applying the chosen forecasting technique.
Include visual representations (graphs, tables) of forecasts and error metrics.
Provide insightful commentary on forecast accuracy and implications.

Business Benefits
Discuss the benefits of this forecasting project for the organization.
Consider financial (e.g., ROI) and/or societal advantages.

Note: Use appropriate software for analysis and ensure clarity in presenting results. Figures and tables should include relevant captions for readability.