Evaluating forecasting-based analytics assessment

Reference no: EM133561665 , Length: word count:1000

Data-driven Decision Making and Forecasting

Assessment - Evaluating forecasting-based analytics

Your Task

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.

Assessment Description
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.

Assessment Instructions

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.

1. Import the DATA4400_A2_Data.csv dataset into Orange Data Mining .

2. Assess the quality of the data in terms of missing values and provide summary statistics of the variables.

3. 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.

4. 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.

5. How do the Fed Funds rate and the unemployment rate affect each other in Genovia?

6. Use Tableau to visualise the dataset and generate a forecast of the unemployment rate at the end of the next quarter.

7. What is the unemployment rate forecasted by Tableau?

8. Explain which model was used in Tableau and report on its parameters.

9. Evaluate the models using the available metrics and report which model provides the best forecast.

10. Summary

Part A: Descriptive Analysis

• Provide summary statistics of each time series data and briefly comment.

• Provide an appropriate visualization of each time series data.

• Does each time series exhibit trend and/or seasonality components?

• What is the frequency of each time series data?

Part B: Holt-Winters Model

• Use the Holt-Winters model to forecast the unemployment rate at the end of the next quarter.

• Provide a screenshot of the Holt-Winters model settings.

• What is the forecast unemployment rate based on the Holt-Winters model?

• Provide the 95% prediction interval of the forecast unemployment rate.

• Report the Holt-Winters model‘s parameters.

Part C: ARIMA Model

• Use an ARIMA model to forecast the unemployment rate at the end of the next quarter.

• Provide a screenshot of the ARIMA model settings.

• What is the forecast unemployment rate based on the ARIMA model?

• Provide the 95% prediction interval of the forecast unemployment rate.

• Report the ARIMA model‘s parameters.

Part D: VAR Model

• Use a VAR model to forecast the unemployment rate at the end of the next quarter.

• Provide a screenshot of the VAR model settings.

• What is the forecast unemployment rate based on the VAR model?

• Provide the 95% prediction interval of the forecast unemployment rate.

• Report the VAR model‘s parameters.

• How do the Fed Funds rate and the unemployment rate affect each other in Genovia?

Part E: Models Comparison

• By splitting the dataset into 90% training and 10% testing, evaluate the forecasting models (Holt- Winters, ARIMA, and VAR) of the unemployment rate using the following criteria RMSE, MAE, and MAPE.
• For each model, provide an appropriate visualization of the forecasts on the testing data.

• Provide a table including RMSE, MAE, and MAPE calculated on the testing data for each model.

• Report and justify which model provides the best forecast for the unemployment rate on the testingdata.

• Provide the Excel file used to generate the calculations of RMSE, MAE, and MAPE.

Reference no: EM133561665

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