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Autocorrelation measures the degree of similarity between a time series and a lagged version of itself over successive time intervals.

It’s also sometimes referred to as “serial correlation” or “lagged correlation” since it measures the relationship between a variable’s current values and its historical values.

When the autocorrelation in a time series is high, it becomes easy to predict future values by simply referring to past values.

How to Calculate Autocorrelation in R

Suppose we have the following time series in R  that shows the value of a certain variable during 15 different time periods:

#define data
x <- c(22, 24, 25, 25, 28, 29, 34, 37, 40, 44, 51, 48, 47, 50, 51)

We can calculate the autocorrelation for every lag in the time series by using the acf() function from the tseries library:

library(tseries)#calculate autocorrelations
acf(x, pl=FALSE)

     0      1      2      3      4      5      6      7      8      9     10 
 1.000  0.832  0.656  0.491  0.279  0.031 -0.165 -0.304 -0.401 -0.458 -0.450 
    11 
-0.369 

The way to interpret the output is as follows:

• The autocorrelation at lag 0 is 1.
• The autocorrelation at lag 1 is 0.832.
• The autocorrelation at lag 2 is 0.656.
• The autocorrelation at lag 3 is 0.491.

And so on.

We can also specify the number of lags to display with the lag argument:

#calculate autocorrelations up to lag=5
acf(x, lag=5, pl=FALSE)

Autocorrelations of series 'x', by lag

    0     1     2     3     4     5 
1.000 0.832 0.656 0.491 0.279 0.031 

How to Plot the Autocorrelation Function in R

We can plot the autocorrelation function for a time series in R by simply not using the pl=FALSE argument:

#plot autocorrelation function
acf(x)

You can also specify a different title for the plot by using the main argument:

#plot autocorrelation function with custom title
acf(x, main='Autocorrelation by Lag')

Additional Resources

How to Calculate Autocorrelation in Python
How to Calculate Autocorrelation in Excel