AUT UniversitySchool of Engineering, Computer and Mathematical SciencesSTAT603: ForecastingAssignment 1The purpose of this assignment is to assess your analytical and computingskills on the material covered.Total Possible Marks: 30 marks, which contribute 15% towards your finalgrade in this paper.Deadline: 5pm, Monday, April 1, 2019Submission: The assignment must be submitted as a soft copy in a single.pdf file including signed SECMS or DCT assignment cover sheet (otherwiseyour 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 submitshis assignment, his .pdf file must be named ”White John 123456789”. Submissionchannel will be announced later.Report/Assignment: Your assignment must be self-contained, i.e., youneed to embed your R code in your answers. See example in the box below:1Page Limit: Maximum number of pages is 10 including graphs and R code.Data: Quarterly total beer available for consumption (million litres) in NewZealand from Quarter 1, 2010 to Quarter 4, 2018(Filename: NZ_TotalBeer_Quarterly.xlsx) Quarterly average nation-wide temperature (degrees celcius) in NewZealand from Quarter 1, 2010 to Quarter 4, 2018(Filename: NZ_AvgTemp_Quarterly.xlsx) Quarterly real national disposable income (Billion NZ dollars) in NewZealand from Quarter 1, 2010 to Quarter 3, 2018(Filename: NZ_DispIncome_Quarterly.xlsx)Note: All data should be converted into time series using ts functionin R.R: All computing tasks must be done using R or RStudio.Plagiarism: If this is the case for your assignment, your case willbe 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 anddescribe the effect. (3 marks)(c) Find and discuss whether the autocorrelation exists in this timeseries. (2 marks)(d) Compute two years of forecasts (i.e. holding the last two years ofdata out as the test set) using the four methods: (1) mean, (2)naive, (3) seasonal naive, and (4) drift. Plot the series and theforecasts, and discuss the results. (8 marks)(e) Compare the root mean squared error (RMSE) of forecasts fromthe four methods in (d). Which method do you think is best forthis time series? (2 marks)22. Time series regression models (13 marks)(a) Fit a regression model to the quarterly total beer available for consumptiondata with a linear trend and seasonal dummies. Discussthe results. (2 marks)(b) Plot the quarterly total beer available for consumption data withthe quarterly average nation-wide temperature and real nationaldisposable income data. Perform the correlation analysis and discussthe results. (3 marks)(c) Fit a regression model to the quarterly total beer available forconsumption data with the quarterly average nation-wide temperatureand real national disposable income data as the explanatoryvariables. Discuss the results. (2 marks)(d) Do we need to include the linear trend and seasonal dummiesin the regression model in (c)? Perform a relevant analysis anddiscuss 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 thosein Question 1 parts (d)-(e). (3 marks)3本团队核心人员组成主要包括硅谷工程师、BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：99515681@qq.com 微信：codehelp
网友评论