Write a research paper describing more about data-driven business decision modelling (data analytics) (forecasting).Consider the two time series data sets below in Tables 1 and 2 where n=24 in both series.
You are required to do the following:
TASK 1 (15 marks) 1. Conduct a diagnostic analysis on both datasets. From these diagnostics Identify which time
series you think is stationary and which you think exhibits trend and seasonality. You should
justify your conclusions with both visual and numerical evidence.
TASK 2 (35 marks) 2. Using the dataset you feel is stationary carry out the following:
a. Use the moving average (MA) approach to smooth the data using a moving average of
period k = 2, 4, 6 and produce a forecast for period n+1 i.e. period 25. Determine which
scheme appears to perform best;
b. Use Solver to produce a weighted moving average for k = 2, 4, 6 with weights optimised
on both MAPE and RMSE to produce a forecast for period n+1. Which scheme appears to
perform the best?
c. Compare your results obtained in a. and b. above,
d. Conduct a similar exercise using exponential smoothing, initially with alpha = 0.2 and 0.8.
Then use Solver to optimise the value of alpha based on both MAPE and RMSE to
produce a forecast for period n+1.
TASK 3 (35 marks)
3. Using the dataset you feel exhibits Trend and Seasonality use an additive decomposition model
to:
a. Extract a seasonal index for each quarter,
b. Deseasonlise the data for each quarter,
c. Produce an unseasonalised and seasonalised forecast for each period,
d. Produce an unseasonalised and seasonalised forecast for periods n+1, n+2, n+3 and n+4,
e. Plot the actual, unseasonalised and seasonalised data on a single graph and comment on
the results;
TASK 4 (15 marks)
4. Consolidate your results in 1 – 3 above into a short report, which should include a critical
evaluation of the methods you have used and consideration of the potential impact on business
strategy of effective use of the forecasting process.
,
a) Upload two spreadsheets with the solutions for each dataset,
b) Upload a Word document containing your report,
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