Predicting future events

There are many techniques we could employ when trying to predict the future, such as moving average (MA), regression, auto-regression, and the like. First, let's start with the simplest one for a moving average:

movingAverageFunction<- function(data,n=10){
  out= data
  for(i in n:length(data)){
    out[i] = mean(data[(i-n+1):i])
  }
  return(out)
}

In the preceding program, the default value for the number of periods is 10. We could use the dataset called MSFT included in the R package called timeSeries (see the code that follows):

> library(timeSeries)
> data(MSFT)
> p<-MSFT$Close
> #
> ma<-movingAverageFunction(p,3)
> head(p)
[1] 60.6250 61.3125 60.3125 59.1250 56.5625 55.4375
> head(ma)
[1] 60.62500 61.31250 60.75000 60.25000 58.66667 57.04167
> mean(p[1:3])
[1] 60.75
> mean(p[2:4])
[1] 60.25

Manually, we find that the average...