This question paper consists of 4 printed pages,
each of which is identified by the Code Number LUBS5309M01
Only calculators from the following list are permitted: Casio fx-82,fx-83, fx-85, fx-350 series Sharp EL-531 series
LUBS5309M
Forecasting and Advanced Business Analytics
UNIVERSITY OF LEEDS May/June 2018
TIME ALLOWED 2 hours Section A (30%): Answer ALL questions
Section B (70%): Answer TWO questions
Marks are shown beside each question / question section.
You may use any of the following software on the computer provided: Microsoft Excel, Microsoft Word, R, R Studio and SPSS.
You can answer the questions in either the booklet provided, a single Microsoft Word document and/ or a single Microsoft Excel file.
Remember to save your Word/Excel files frequently throughout the exam
Data for the questions in Section B will be available within the LUBS5309M module area on MINERVA from the START of the exam.
MINERVA Submission
You should submit your Microsoft Word Document and Microsoft Excel File via the Turnitin submission link in the LUBS5309M module area on MINERVA at the END of the exam.
Log into MINERVA, navigate to the LUBS5309M module area and click on Assessment. Submit the excel file to LUBS5309M exam EXCEL submission area by clicking on View/Complete, and submit the word file to LUBS5309M Exam WORD submission area by clicking on View Complete.
Be patient when opening applications; constantly re-opening an application when it hasn’t opened will slow the system down.
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Section A – Answer ALL questions
A1. Explain how the moving average method uses n observations to smooth time series
data. What would be the difference in using n = 3 compared to n = 20?
[10 Marks]
A2. Describe how simulated annealing works. Explain how the temperature variable and
greed works in your answer.
A3. Explain what deep learning is and give examples of how it is being used.
A4. List the conditional probabilities for the following Bayesian network:
[12 Marks] [5 Marks]
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[3 Marks] Total 30 Marks
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B1. a.
b.
Section B – Answer TWO questions only.
Your manager has created a function to model a business process and wants to use
a genetic algorithm to identify the minimum solution.
Describe how genetic algorithms work. Explain the crossover and mutation stages in your answer giving examples with binary encoding.
[10 Marks]
Solve the function below with a genetic algorithm using the ga function in the GA package in R. Use a real-valued optimisation type and set the minimum input parameters as c(-10, -10) and the maximum input parameters as c(10, 10). Include the code used, a plot of the fitness value throughout the GA generations, the summary output of the function and an explanation of the summary output.
B2.
a.
b.
c.
Include the code used and the summary output of the function. Describe what these custom parameters are used for and how they have affected the result in comparison to your previous answer.
[10 Marks] Total 35 Marks
Iveco has approached your consultancy company asking you to help them forecast the number of 35S12 vans sold in the UK in the next year. They have provided you with the quarterly time series sales data from Q3 2008 to Q3 2017 (B2.csv) for the Iveco Daily 35S12 van.
Using the read.csv, ts and plot functions in R, import the data, create a time series object, and then plot the time series object.
From looking at this plot, what can you say about the trend and seasonality of the data? Include the plot in your answer.
[10 Marks]
Using the plot and stl functions in R, decompose the data with loess (additive) decomposition and explain what is shown in the plot. Explain what the bars to the right of the plot represent.
[10 Marks]
Using the ets() function in the forecast package in R, predict future sales using exponential smoothing for the next year (4 observations only). Set alpha so that you give more weight to more recent observations. Include an image of the forecast in your answer.
[15 Marks]
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c.
B1 <- function(x) {
sum <- sum(x^4 - 16*x^2 + 5*x)
return(-sum/2) }
Repeat the ga optimisation with the following custom parameters: popSize = 100
pcrossover = 0.9 pmutation = 0.2 maxiter = 500
[15 Marks]
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B3.
a.
b.
c.
d.
HR has approached you to help them study your company’s employees. They have provided you with a dataset (B3.csv) with the following 6 columns about 14,999 employees:
satisfaction_level: last_evaluation: number_project: average_montly_hours: time_spend_company: Work_accidents:
Satisfaction Level
Last evaluation
Number of projects
Average monthly hours
Time spent at the company
Number of accidents the employee has had at work
Describe the differences between Principal Components Analysis (PCA) and Exploratory Factor Analysis (EFA).
[8 Marks]
Using read.csv and the corrgram function from the corrgram package, import the data and create a correlogram plot of the 6 measurements of the employees. Discuss the suitability of the data for PCA and include an image of the plot in your answer.
[8 Marks]
Using the plot and prcomp functions in R, plot a scree plot and describe how you can use this plot to identify the number of components to use in principal components analysis. Include the scree plot in your answer.
[9 Marks]
Using the prcomp function in R, use principal components analysis on the data. Include and describe the results of the analysis. Discuss the loadings and how appropriate it would be to use two components.
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[10 Marks] Total 35 Marks
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