Given a dataset with multivariate time series data, you are to conduct multiple forecasting methods and provide a description and interpretation of the techniques used. The report is worth 30 marks (see rubric for the allocation of these marks).
A dataset will be provided to you at the beginning of week 9. The objective of the assessment is to build different forecasting models using Orange Data Mining and Tableau. Students must calculate the Root Mean Square Error (RMSE) or Mean Absolute Percentage Error (MAPE) to evaluate the performance and accuracy of the model, as well as choose the appropriate metrics for model selection.
Report Structure and Content
Imagine you work for the Central Bank of Genovia and your task is to forecast the unemployment rate in one quarter.
- Import the DATA4400_A2_Data.csv dataset into Orange Data Mining (https://orangedatamining.com/).
- Assess the quality of the data in terms of missing values and provide summary statistics of the variables.
- Using an ARIMA model, forecast the unemployment rate for one quarter. (A) What is the forecast unemployment rate based on the ARIMA model? b. Provide a screenshot of the ARIMA model settings and the appropriate visualisation for your forecast.
- Using a VAR model, forecast the unemployment rate for one quarter. (A) What is the forecast unemployment rate based on the VAR model? (B)Provide a screenshot of the VAR model settings and the appropriate visualisation for your forecast.
- How do the Fed Funds rate and the unemployment rate affect each other in Genovia?
- Use Tableau (https://www.tableau.com/academic/students) to visualise the dataset and generate a forecast of the unemployment rate at the end of the next quarter.
- What is the unemployment rate forecasted by Tableau?
- Explain which model was used in Tableau and report on its parameters.
- Evaluate the models using the available metrics and report which model provides the best forecast.
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