You have been employed as a consultant for the Australian Retailers Association. As part of your role in the Business Analytics and Data Analytics team, you have been asked to forecast business turnover for retail trade, as part of a wider report being commissioned by the Australian Retailers Association - on Australia's retail industry. Questions Obtain the ABS statistics for Business Turnover Index ; Retail trade ; Current Price - available at: https://www.abs.gov.au/statistics/economy/business-indicators/monthly-business-turnover-indicator/jan-2022

Download Table 06. For the purposes of this report you are to consider the Business Turnover Index; Retail trade; Current Price data. There are three series in the table: Original, Seasonally-adjusted, and Trend (please choose carefully throughout this report!) For the purposes of this report, only consider the data from February 2011 to January 2021 as the sample of data that is available to you - that is, ignore any recent observations. This means that the first actual observation in your Excel file is from February 2011 and your last actual observation in your Excel file is from January 2021. Use Excel and no other statistical software for the purposes of this report. You may use Minitab for constructing correlograms. This report will require two separate submissions. The numerical responses need to be submitted via a quiz tool in iLearn. The written responses need to be submitted via a PDF uploaded via Turn-It-In in iLearn.

Notes for Exponential Smoothing Models for this Report If you use any Exponential Smoothing Models in this Report, please note: For Simple Exponential Smoothing - for the seed of the level use the first observation, Y1. For Holt's Exponential Smoothing - for the seed of the level use the first observation, Y1. For the seed of the trend - take the difference of the first two observations (Y2 - Y1). For Winters' Exponential Smoothing - for the seeds of the level, trend, and seasonal components - utilise the methods described and discussed in class. Choose Multiplicative over Additive models where applicable.

Numerical responses to be submitted via a quiz tool on iLearn: Exercise 1 - Application (10 marks) For the purposes of this report, only consider the data from February 2011 to January 2021 as the sample of data that is available to you - that is, ignore any recent observations. This means that the first actual observation in your Excel file is from February 2011 and your last actual observation in your Excel file is from January 2021. For the Seasonally-adjusted data for Business Turnover Index; Retail trade; Current Price (Series ID: A124873965J) available in Table 6: Forecast the out-of-sample values for every month in the period February 2021 - July 2022 (both months inclusive) using one appropriate exponential smoothing model. Your starting values for any parameter should be 0.25. Please see the notes on page 4 of this document - regarding seeds. Before you begin Exercise 1, let's check that you have the right data! The average should be 89.7! Once you develop an appropriate exponential smoothing model with starting values for parameter/s = 0.25, what are the following numerical values: 1. The within-sample forecast for January 2021. 2. The MSE. 3. The MAE. 4. The out-of-sample forecast for February 2021. 5. The out-of-sample forecast for July 2022. By considering the MSE, critically think of a way to optimise the model by altering the parameters, and report the following values after your optimisation (your answer can be zero if a parameter is not applicable): 6. Alpha. 7. Beta. 8. Gamma. 9. The MSE. 10. The out-of-sample forecast for July 2022.