The autocorrelation function

Autocorrelations can be estimated at many lags to better assess how a time series relates to its past. We are typically most interested in how a series relates to its most recent past.

The acf(..., lag.max = ..., plot = FALSE) function will estimate all autocorrelations from 0, 1, 2,…, up to the value specified by the argument lag.max. In the previous exercise, you focused on the lag-1 autocorrelation by setting the lag.max argument to 1.

In this exercise, you'll explore some further applications of the acf() command. Once again, the time series x has been preloaded for you and is shown in the plot on the right.

This exercise is part of the course

Time Series Analysis in R

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Exercise instructions

Use acf() to view the autocorrelations of series x from 0 to 10. Set the lag.max argument to 10 and keep the plot argument as FALSE.
Run the acf() code, and copy and paste the autocorrelation estimate (ACF) at lag-10 from the output.
Repeat this for the autocorrelation estimate (ACF) at lag-5.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Generate ACF estimates for x up to lag-10
acf(___, lag.max = ___, plot = FALSE)

# Type the ACF estimate at lag-10 


# Type the ACF estimate at lag-5

Edit and Run Code