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DATA4400 2024 Trimester 2:

Questions
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).
a) 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.
[3 marks]
b) 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.
[3 marks]
c) 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).
[4 marks]
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.]
e) Comment briefly on the RMSEs in Tables 1,2.
[3 marks]
f) 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

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