讲解：STATS 326、R、data、RC/C++|Database

STATS 326Applied Time SeriesASSIGNMENT ONEDue: 20 March, 12.00Worth 6% of your final grade (727 4%)Hand-in to the appropriate STATS 326 Hand-in box in the Student Resource CentreFor each of the first 4 questions in this assignment you are required to find a Univariate TimeSeries, describe the Time Series you are using, state where you found the data (web address isfine), do a Time Series plot of the data in R (see page 14 of the Course Notes) and describethe main features you see in the plot.Each Time Series you use can come from the web, journal articles, text books etc. You mustNOT use any Time Series that are used as examples in the course (see page v of your CourseNotes for a list of the Time Series data sets that are used as examples in the Lecture Notesand page 243 for a list of the Tutorial data sets). Using a Time Series used in the course withthe time range changed (reduced or increased) is not acceptable. It would also be advisable tolook at pages 55 – 59 and pages 244 – 248 before commencing this assignment.This assignment will be marked out of 100 with each question worth 20 marks. Each questionshould take no more than 1 side of A4 paper. You are encouraged to print your assignment“2-up” to save paper.Question One: [20 marks]Find a Time Series that exhibits cycles. (See pages 55 – 58 of the Course Notes.)Question Two: [20 marks]Find a Stationary Time Series. (See page 3 of the Course Notes.)Question Three: [20 marks]Find a Time Series that has a seasonal cSTATS 326作业代做、R课程设计作业代做、data留学生作业代写、R编程语言作业调试 调试C/C++编程|代写Daomponent but no trend or cycle. (See pages 55 – 59 ofthe Course Notes.)Question Four: [20 marks]Find a Time Series that has a reasonably linear trend and a seasonal component. (See page 55and pages 58 – 59 of the Course Notes.)Question Five: [20 marks]The data contained in the file “Cape Grim CO2 2000.1 - 2019.9.txt” records the averageconcentration of CO2 in the atmosphere at Cape Grim, Tasmania, Australia for each monthfrom January 2000 to September 2019.Load the data into R, create a “time series object” and produce a Time Series plot. Copy theplot into your assignment.Using the aggregate function in R, convert the data into the average concentration of CO2in the atmosphere for each quarter from 2000 to 2019.3. (Include the R commands you usedto aggregate the data in your assignment.) Plot the quarterly series, copy the plot into yourassignment and describe the plot.Data Source:https://www.esrl.noaa.gov/gmd/dv/data/index.php?category=Greenhouse%2BGases¶meter_name=Carbon%2BDioxide&frequency=Monthly%2BAverages&site=CGOHINT: See page 233 of the Course Notes. Here we aggregate monthly data into annual data.The original frequency was 12 and the new frequency is 1 (in R: nfrequency = 1which is the default setting so was not required as an argument in the R command).Our data for this question is monthly (frequency = 12). The new frequency is 4 (inR: nfrequency = 4).NOTE: Check that the quarterly averages appear consistent with the original monthlyaverages. 转自：http://www.6daixie.com/contents/18/4978.html