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introduction to time series in r

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1 Introduction to basic time series functions in R This chapter introduces you to some of the basic functions in R for plotting and analyzing univariate time series data. Many of the things you learn here will be relevant when we start examining multivariate time series as well. We will begin with the creation and plotting of time series objects in R, and then moves on to decomposition, differencing, and correlation (e.g., ACF, PACF) before ending with fitting and simulation of ARMA models. 1.1 Time series plots Time series plots are an excellent way to begin the process of understanding what sort of process might have generated the data of interest. Traditionally, time series have been plotted with the observed data on the y-axis and time on the x-axis. Sequential time points are usually connected with some form of line, but sometimes other plot forms can be a useful way of conveying impor- tant information in the time series (e.g., barplots of sea-surface temperature anomolies show nicely the contrasting El Ni˜ no and La Ni˜ na phenomena). Let’s start by importing some data; the record of the atmospheric con- centration of CO 2 collected at the Mauna Loa Observatory in Hawai’i makes a nice example. The data file contains some extra information that we don’t need, so we’ll only read in a subset of the columns (i.e., 1, 2 & 5). # get CO2 data from Mauna Loa observatory ww1 <- "ftp://aftp.cmdl.noaa.gov/products/" ww2 <- "trends/co2/co2_mm_mlo.txt" CO2 <- read.table(paste(ww1,ww2,sep=""))[,c(1,2,5)] # assign better column names colnames(CO2) <- c("year","month","ppm")

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Time series in R

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1IntroductiontobasictimeseriesfunctionsinRThischapterintroducesyoutosomeof thebasicfunctionsinRforplottingandanalyzingunivariatetimeseriesdata.Manyofthethingsyoulearnherewillberelevantwhenwestartexaminingmultivariatetimeseriesaswell.WewillbeginwiththecreationandplottingoftimeseriesobjectsinR,andthenmovesontodecomposition, dierencing, andcorrelation(e.g., ACF, PACF)beforeendingwithttingandsimulationofARMAmodels.1.1TimeseriesplotsTimeseriesplotsareanexcellentwaytobegintheprocessofunderstandingwhatsortofprocessmighthavegeneratedthedataofinterest.Traditionally,timeserieshavebeenplottedwiththeobserveddataonthe y-axisandtimeon the x-axis. Sequential time points are usually connected with some form ofline,butsometimesotherplotformscanbeausefulwayofconveyingimpor-tantinformationinthetimeseries(e.g.,barplotsofsea-surfacetemperatureanomoliesshownicelythecontrastingElNi noandLaNi naphenomena).Letsstart byimportingsomedata; therecordof theatmosphericcon-centrationofCO2collectedattheMaunaLoaObservatoryinHawaiimakesaniceexample.Thedatalecontainssomeextrainformationthatwedontneed,sowellonlyreadinasubsetofthecolumns(i.e.,1,2&5).# get CO2 data from Mauna Loa observatoryww1


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