The R Book

Moving Average

The simplest way of seeing pattern in time series data is to plot the moving average. A useful summary statistic is the three-point moving average:

The function ma3 will compute the three-point moving average for any input vector x:

ma3<-function (x) {
  y<-numeric(length(x)-2)

for (i in 2:(length(x)-1)) {
  y[i]<-(x[i-1]+x[i]+x[i+1])/3
}
y }

A time series of mean monthly temperatures will illustrate the use of the moving average:

temperature<-read.table("c:\\temp\\temp.txt",header=T)
attach(temperature)
tm<-ma3(temps)
plot(temps)
lines(tm[2:158])

The seasonal pattern of temperature change over the 13 years of the data is clear. Note that a moving average can never capture the maxima or minima of a series (because they are averaged away). Note also that the three-point moving average is undefined for the first and last points in the series.