STAT603留学生代做、R程序语言调试、代做R、代写SECMS

日期：2019-03-27 10:18

AUT University

School of Engineering, Computer and Mathematical Sciences

STAT603: Forecasting

Assignment 1

The purpose of this assignment is to assess your analytical and computing

skills on the material covered.

Total Possible Marks: 30 marks, which contribute 15% towards your final

grade in this paper.

Deadline: 5pm, Monday, April 1, 2019

Submission: The assignment must be submitted as a soft copy in a single

.pdf file including signed SECMS or DCT assignment cover sheet (otherwise

your assignment won’t be marked). Your filename must include 1) your lastname,

2) your firstname, and 3) your student id, e.g., if John White submits

his assignment, his .pdf file must be named ”White John 123456789”. Submission

channel will be announced later.

Report/Assignment: Your assignment must be self-contained, i.e., you

need to embed your R code in your answers. See example in the box below:

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Page Limit: Maximum number of pages is 10 including graphs and R code.

Data:

Quarterly total beer available for consumption (million litres) in New

Zealand from Quarter 1, 2010 to Quarter 4, 2018

(Filename: NZ_TotalBeer_Quarterly.xlsx)

Quarterly average nation-wide temperature (degrees celcius) in New

Zealand from Quarter 1, 2010 to Quarter 4, 2018

(Filename: NZ_AvgTemp_Quarterly.xlsx)

Quarterly real national disposable income (Billion NZ dollars) in New

Zealand from Quarter 1, 2010 to Quarter 3, 2018

(Filename: NZ_DispIncome_Quarterly.xlsx)

Note: All data should be converted into time series using ts function

in R.

R: All computing tasks must be done using R or RStudio.

Plagiarism: If this is the case for your assignment, your case will

be referred to an appropriate university’s office.

Tasks/Questions:

1. Use the quarterly total beer available for consumption data. (17 marks)

(a) Plot the series and discuss the main features of the data. (2 marks)

(b) Discuss whether a transformation is needed. If yes, do so and

describe the effect. (3 marks)

(c) Find and discuss whether the autocorrelation exists in this time

series. (2 marks)

(d) Compute two years of forecasts (i.e. holding the last two years of

data out as the test set) using the four methods: (1) mean, (2)

naive, (3) seasonal naive, and (4) drift. Plot the series and the

forecasts, and discuss the results. (8 marks)

(e) Compare the root mean squared error (RMSE) of forecasts from

the four methods in (d). Which method do you think is best for

this time series? (2 marks)

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2. Time series regression models (13 marks)

(a) Fit a regression model to the quarterly total beer available for consumption

data with a linear trend and seasonal dummies. Discuss

the results. (2 marks)

(b) Plot the quarterly total beer available for consumption data with

the quarterly average nation-wide temperature and real national

disposable income data. Perform the correlation analysis and discuss

the results. (3 marks)

(c) Fit a regression model to the quarterly total beer available for

consumption data with the quarterly average nation-wide temperature

and real national disposable income data as the explanatory

variables. Discuss the results. (2 marks)

(d) Do we need to include the linear trend and seasonal dummies

in the regression model in (c)? Perform a relevant analysis and

discuss the results. (3 marks)

(e) Compute two year of forecasts for the regression models in (a)

and (c). Evaluate the forecast accuracy and compare with those

in Question 1 parts (d)-(e). (3 marks)

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