1) Import Data File

1) Import data file.

lynx.column.txt or lynx.row.txt

2) Convert data.frame to a vector (or a matrix for the case of multivariate TS)

data = as.vector(lynx.column[,1])

data = as.vector(lynx.row[1,])

3)Create TS

Basic functions :

1. rts() - Defines a univariate or multivariate regularly spaced time series

USAGE:

rts(x = NA, start = 1, deltat = 1, frequency = 1, end = <see below>, units = NULL, names = NULL, eps = .Options$ts.eps)

EXAMPLES:

lynx.rts = rts(x = data, start = c(1990, 2), frequency=12) #deltat = 1/12 = 1 month

x <- rts(rnorm(100), start = c(1953, 4), frequency = 12)

is.rts(x)

lynx.rts <- as.rts(lynx)

corn.rts <- rts(cbind(corn.rain, corn.yield), start = 1890,

units = "years", names = c("rain", "yield"))

2. its() - Constructs a univariate or multivariate time series with arbitrary sampling time intervals.

USAGE:

its(x, times, units = NULL, names = NULL)

is.its(x)

EXAMPLES:

whitenoise <- matrix(rnorm(50), ncol = 2)

obs.times <- sort(runif(25, 0, 10)) # make up some observation times

x <- its(whitenoise, times = obs.times, names = c("error1","error2"))

is.its(x)

birthdays <- dates(c("01/02/78", "10/21/81", "06/23/86",

"01/19/90", "06/29/92"))

ages <- c(15, 11, 7, 3, 1)

its(ages, times = birthdays)

2. cts() - Defines a regular univariate or multivariate time series object with calendar dates associated with the observations

USAGE:

cts(x, start = dates("01/01/60"), units = "years",

k.units = 1, frequency = 1, names = NULL)

is.cts(x)

4. timeSeries() - Construct a timeSeries object from positions and data, or return an empty timeSeries

USAGE:

timeSeries()

timeSeries(data, positions., units., from=timeCalendar(d=1,m=1,y=1960),

by="days", k.by=1, align.by=F, week.align=NULL)

EXAMPLES:

timeSeries()

timeSeries(data= data.frame(x = 11:20, y = 21:30), from="1/1/98" )

5. signalSeries() - Construct a signalSeries object from positions and data, or return an empty signalSeries object.

USAGE:

signalSeries()

signalSeries(data, positions., units, units.position, from=1, by=1)

EXAMPLES:

signalSeries(data = data.frame(x = 11:20), from = 1, by = 1)

4) Plotting TS :

plot.timeSeries()

USAGE:

plot.timeSeries(x, ..., main=x@title, ylab=x@units,

top.ticks=F, right.ticks=F, reference.grid=T,

plot.type="lines",

merge.args=list(pos="union", how="interp"),

x.axis.args=list(), y.axis.args=list(),

plot.args=list(), log.axes="c", complex.convert=Mod,

frame=sys.nframe())

EXAMPLES:

djia1 <-djia[positions(djia)>=timeDate("09/01/87") &

positions(djia)<=timeDate("11/01/87"), 1:4]

plot.timeSeries(djia1, plot.type="hloc")

plot.timeSeries(djia1, plot.type="lines")

or just

plot(djia1, type=”l”) # may take timeSeries object (TS or SS)

5) Analysing TS :

1. diff() - Returns a time series or other object which is the result of differencing the input.

USAGE:

diff(x, lag=1, differences=1)

2. acf() - Estimates and displays autocovariance, autocorrelation or partial autocorrelation functions.

USAGE:

acf(x, lag.max=<see below>, type="correlation", plot=T)

EXAMPLES:

acf.lynx <- acf(lynx, 36, "correlation")

corn.rts <- rts(cbind(corn.rain, corn.yield), start=1890,

units="years", names=c("rain", "yield") )