2019S1 QBUS6840

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2019S1 QBUS6840 Assignment 1 Page 1 of 5
QBUS6840 Assignment 1 – Homework:
Due dates: Friday 12 April 2019
Value: 15%
Rationale
This assignment has been designed to help students to develop basic predictive analytics
skills on synthetic and possible real applied problems, including data visualization, model
building and analysis in terms of understanding in theory, practices with raw data and
programming in Python.
Tasks

1. Consider the (odd order) centred MA-(2+ 1) (i.e. CMA-(2 + 1)) and the two layer
   (2m+1)x(2n+1)-MA.
   (a) Show that a 3x5-MA is equivalent to a 7-term weighted moving average and find out
   all the weights. For general nonnegative integers m and n, argue that a
   (2m+1)x(2n+1)-MA is equivalent to a X-term weighted moving average. What is X?
   (b) Write out the formula for the CMA-(2 + 1), and use your general formula to write
   out the formula for CMA-11.
   (c) Prove that when the given time series is periodic with the period 2+ 1, the
   smoothed time series by the CMA-(2 + 1) is a constant series. Find out the
   value of that constant.
   (d) Again assume that the time series is periodic with the period 2???? + 1. Its first
   order difference time series is defined as.
   Prove that the new time series is also periodic with the period M, and identify
   the smallest value for M.
   Apply CMA-(M) to and find out the resulting smoothed time series
   You must clearly show each step of reasoning.

   [25 Marks]

2. The data set CBA_1991-2018.csv on Canvas (data was downloaded from
   https://au.finance.yahoo.com/... contains the monthly stock
   prices of Commonwealth Bank of Australia (CBA) from August 1991 to December 2018.
   2019S1 QBUS6840 Assignment 1 Page 2 of 5
   (a) Write Python script to load the data and extract High stock prices and make it as a
   time series with Datetime as index and store it as a new csv file CBA_1991-
   2018High.csv.
   Transform the time series data by the first order and the second order differencing and
   produce their plots (three plots) in order to become familiar with it. Include the plots
   in your submission. You must use Datetime index as the x-axis of your plots.
   (b) Write your own Python script to implement smoothing using the CMA-24 method and
   plot the smoothed time series of the original time series series in (a) against it. And
   write Python code to use pandas package’s rolling_mean function (ver 0.17) or
   rolling function (ver 0.20+) to re-do the CMA-24 smoothing. Compare results of
   your own implementation and the results of pandas implementation. Have you got
   the same results? Why? Please refer to pandas documentation regarding how to use
   rolling or rolling_mean.
   (c) Report the scale-dependent measures Root Mean Squared Error (RMSE) and Mean
   Absolute Error (MAE) for the methods in (b) [the errors between your smoothed
   prices and the true prices (be careful of missing smoothed values at the beginning
   and/or the ending sides!)].
   (d) The CMA-5 smoothing can be turned into a forecasting method to do one-step ahead
   forecasting as follows
   Use this forecasting method to forecast the last four months in the time series of (a)
   (i.e., we assume we don’t know them when doing forecasting). Write your own
   Python program for the task.
   (e) It may not be of much accuracy using the CMA-5 forecasting method for a given time
   series. However, for the time series in (a), you may seek for a forecasting method
   defined as, by using linear regression.
   For the given time series in (a), formulate a least squared linear regression problem
   and write your Python program to implement this regression task to work out weights. You may use all the data except for the last four months in the
   time series of (a).
   With the newly learned weights, do one-step ahead forecasting for
   the last four months.
   Hint: Given the special condition 4 = 1 on
   2019S1 QBUS6840 Assignment 1 , you may design your regression problem such that there are only 4
   weights (e.g.,to be solved. Think about what the training data should
   be in this case.
   (f) Report the scale-dependent measures Root Mean Squared Error (RMSE) and Mean
   Absolute Error (MAE) for the methods in (d) and (e) [the errors between predicted
   prices and the true prices.].
   [25 Marks]
3. Consider the dataset plastic.csv which consists of the monthly sales (in thousands)
   of product A for a plastics manufacturer for fives years.
   (a) Plot the time series of sales of product A. Analyze and identify seasonal fluctuations
   and/or a trend-cycle?
   (b) Write your own Python program to implement the classical multiplicative
   decomposition to calculate the trend-cycle and seasonal indices. Discuss whether
   the results support the graphical interpretation from part (a).
   (c) Compute and plot the seasonally adjusted data.
   (d) Change one observation to be an outliner (e.g., add 500 to one observation), and
   recompute the seasonally adjusted data. What is the effect of the outlier?
   (e) To use the decomposition for forecasting, build a regression model for the trendcycle
   component, and then use this trend-cycle components and other components to
   make three forecasts (one-step ahead, two-step ahead and three-step ahead
   predictions).
   [20 Marks]
4. The data set Airline.csv is a famous time series of monthly total international airline
   passengers from Jan 1949 - Dec, 1960. You are required to forecast the next four
   months’ passenger numbers via using relevant models or methods as specified in the
   following tasks:
   (a) Plot the series in your Python program and discuss the main features of the data.
   (b) Write your own Python script to implement the Holt’s linear trend method on the
   Airline series. You may follow the Component form at
   https://otexts.com/fpp2/holt.... to define a Python function which takes at least
   three arguments, i.e., the time series y, the smoothing parameter for level α and the
   smoothing parameter for the trend β, and returns the smoothed time series. Make
   your argument on setting a reasonable value for ????0 and ????0, respectively. In your code,
   explore the combination of different values of α and β e.g. 0.2, 0.4. 0.6 and 0.8.
   Calculate and record the one-step ahead SSE (sum of the squared errors) for each pair
   of values α and β. Choose Four representative smoothed series to plot and use the
   legends to indicate corresponding α and β values and SSE. Discuss the effect of α and
   β on the forecasts based on the 16 cases, report which values of α and β work best
   2019S1 QBUS6840 Assignment 1 Page 4 of 5
   among 16 cases, and predict what the optimal α and β could be.
   (c) The Holt’s linear trend method also provides multi-horizon forecast, please refer to
   https://otexts.com/fpp2/holt.... In your Python program, write code to select the
   optimal values of α and β with respect to the two-step ahead (or horizon) forecast
   SSE. Plot the SSE for the two-step ahead (horizon) forecast against α and β. Use the
   optimal two-step ahead α and β to generate forecasts for the next four Months. Plot
   the original data series and the smoothing series based on the optimal two-step ahead
   alpha α and β with all the forecasts, against each other.
   Hint: This is a 3D plot and you will need to iterate over a range of α and β values
   [30 Marks]
   Tips for Tasks
5. In your program, you may include the following code to implement SSE.
   def sse(x, y):
   return np.sum(np.power(x - y,2))
6. In Task 3, you may need build a linear regression model. This can be easily done by
   using Python sklearn package (a machine learning package). The following code
   section would be helpful
   from sklearn import linear_model
   lm = linear_model.LinearRegression(fit_intercept=True)
   model = lm.fit(X,y) % Fitting linear model to data
   forecasts = lm.predict(x) % times series forecasting
   where X and y are input and dependence variables respectively.
7. In answering question (c) in Task 4, you may produce about 100 alpha and 100 beta
   values, respectively, by using
   alphas = np.arange(0.01,1,0.01)
   betas = np.arange(0.01,1,0.01)
   Presentation
   Please submit your project through the electronic system on the Canvas.
   The assignment material to be handed in will consist of a PDF or WORD document that:
   i) Details ALL steps.
   ii) Demonstrates an understanding of the relevant principles of forecasting by showing
   your analysis and calculation.
   2019S1 QBUS6840 Assignment 1 Page 5 of 5
   iii) Clearly and appropriately presents any relevant tables, graphs and screen dumps from
   programs if any.
   iv) Provide your program code (if any) as separated py file(s). You will be instructed
   how to submit your program code files.
   Late Penalty
   The assignment is due at Friday 16:00pm 12 April 2019. The late penalty for the assignment
   is 5% of the assigned mark per day, starting after 16:00 pm on the due date. The closing date,

8. April 2019, 16:00pm is the last date on which an assessment will be accepted for marking.

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