Comment on features of the two time series

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Reference no: EM133746772

Data-driven Decision Making and Forecasting

Your Task
Apply forecasting techniques to a given dataset and provide a business application of the forecasts.

Assessment Description

The data provided for the assessment are time series of monthly sales revenue for two stores: TheDon which sells cricket equipment; and WorkOut which sells equipment for a wide range of sports.'

The objective of the assessment is to use Exploratory, Tableau and Excel software to: describe the time series; to develop Prophet and Holt-Winters demand forecast models for the two stores; and to compare forecasts from these models.

Assessment Instructions

In class: You will be presented with a dataset in class. As a group, analyse the dataset using Tableau and Exploratory.io.

As a group:

Part A

Use Tableau to compare time series plots for the two stores by: plotting one under the other; and by plotting both on the same graph. Explain how you implement these plots in Tableau.
Comment on features of the two time series. Use descriptive analytics to compare the two stores in terms of sales revenue, and explain how these statistics can be obtained using Tableau.

Part B

Use Tableau to investigate trend and seasonal variability in the time series for the two stores. Explain how to implement relevant graphics in Tableau. Comment on the trends and seasonal effects.

Part C

Use Tableau to obtain Holt-Winters forecasts for TheDon for the next 12 months, and to provide a suitable graphical display. Explain the various summaries provided by Tableau, in the context of TheDon. Explain how to find the point forecast three months ahead and its 95% Prediction Interval in Tableau.

Part D

Use Exploratory to obtain Prophet forecasts for TheDon for the next 12 months, and to provide a suitable graphical display. Explain the various summaries provided by Exploratory, in the context of TheDon. Explain how to find the point forecast three months ahead and its 95% Prediction Interval in Exploratory.

Part E

Prepare a PowerPoint presentation with your answers to A, B, C, and D:
Include screenshots showing how you use the software, and of the information provided by the software.
Discuss the suitability of HW and Prophet forecasting for TheDon.

As individuals:

Take the most recent year as a test series. Use your individual time series, less the final year, as a training series.
a) Plot training series plus HW forecasts for test year for TheDon in Tableau.
b) Plot training series plus Prophet forecasts for test year for TheDon in Exploratory.
c) Selecting from statistics given in Tableau and Exploratory, construct a table of RMSE within training series by method by store.
d) Make calculations in Excel, and construct a table of RMSE in the test series by method by store.
e) Explain why the RMSE is expected to be larger in the test series. Will the RMSE necessarily be larger in the test series?
f) Make calculations in Excel, and find the MASE using Prophet in the test series for WorkOut.

General Instructions

Go to Learning Forum> Week 5 TIME SERIES and download:

D44_A1_DATAGENERATOR

Open D44_A1_DATAGENERATOR. The first four columns (A, B, C, D) are: t (month number); date; BWsales (BarrelWave sales); GLsales (GuyLine sales). There are 133 rows corresponding to labels plus 132 months (11 years) from January 2013 up to December 2023. The unit of measurement of sales is Lakh Erehwon $ per month (LE$/month). D44_A1_DATAGENERATOR includes a random number generator so it will be different each time it is opened. You need to set up a file that does not change by copying the columns as numeric. Follow the instructions below.

Open a new Excel file and Copy & Paste123 the first four columns of D44_A1_DATAGENERATOR into your new file. Call this new file D44_A1_#######, where ####### is your student number. Your file will be different from anyone else's.
Make two copies of D44_A1_#######. Call the first copy TRAIN and delete the rows corresponding to 2023. So TRAIN contains data for 2013-2022. Call the second copy TEST and remove the rows corresponding to years 2013-2022, so that TEST has 13 rows: labels, plus monthly values for 2023.

You read TRAIN into Exploratory and Tableau and make forecasts for 2023. You export the forecasts into TEST and then calculate the forecast errors and statistics of the forecast errors using Excel.

Question

NOTE - the figure and table captions should include: details of the variables including the unit of measurement (e.g. Sales (monthly total, LE$); the time period for observations and the time increment (e.g. month from January 2013- December 2022); and if appropriate the time period of forecasts and the name of the forecasting technique (e.g. HW forecasts for 2023).

Plot TRAIN sales time series plus Holt-Winter (HW) forecasts for 2023 for BarrelWave in Tableau. Save the graph. Import the graph into your report and label it as Figure 1, with a detailed caption.

Plot TRAIN sales time series plus Prophet forecasts for 2023 for BarrelWave in Exploratory. Save the graph. Import the graph into your report and label it as Figure 2, with a detailed caption.

Selecting from statistics given in Tableau and Exploratory, construct a 2 by 2 table with rows BarrelWave, GuyLine, and columns HW, Prophet containing the within TRAIN RMSE. [NOTE You need to make forecasts for GuyLine to obtain the RMSE and for Parts d) f), but you are not asked to provide graphs for GuyLine.]

Label this table as Table 1 and provide a detailed caption that distinguishes it from d).

Make calculations of TEST RMSE using Excel and construct a 2 by 2 table with rows BarrelWave, GuyLine, and columns HW, Prophet containing the TEST RMSE. Label this as Table 2 and provide a detailed caption that distinguishes it from the table in c).[4 marks - subject to submission of your TEST Excel file.]Comment briefly on the RMSEs in Tables 1,2.

Make calculations in Excel, or otherwise, find the MASE using Prophet in the TEST series for GuyLine. The mean absolute scaled error (MASE) is the ratio of the MAE using Prophet to the MAE using a modified naïve forecast, suitable for highly seasonal time series when seasonal variation dominates any trend: the naïve forecast for January 2023 sales is January 2022 sales, the naïve forecast for February 2023 sales is February 2022 sales, and so on until the naïve forecast for December 2023 sales is the December 2022 sales. Comment on your MASE value.

Reference no: EM133746772

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