How to Calculate Cross-Correlation in R
How to Calculate Cross-Correlation in R, The degree of resemblance between a time series and a lagged version of another time series is measured using cross-correlation.
In another way, it can tell us whether one-time series is a leading signal for another.
Cross-correlation is used in different areas like economics, business, Biology, etc…
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Some of the common examples are given below.
1) The consumer confidence index (CCI) is regarded as a leading indicator of a country’s gross domestic product (GDP).
2) Marketing spend is sometimes regarded as a leading indicator of a company’s future revenue.
3) The total amount of pollution in the water is thought to be a leading indicator of the population of a particular turtle species.
In this tutorial, we are going to describe how to measure the cross-correlation between two time series in R.
Cross-Correlation in R
Let’s create a business example suppose the company spends on marketing and the revenue gained in that period.
#create data
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Spend <- c(5, 3, 6, 5, 8, 9, 10, 17, 12, 11, 10, 9) Income <- c(25, 29, 22, 34, 22, 28, 29, 31, 34, 45, 45, 40)
We can calculate the cross-correlation for every lag between the two-time series by using the ccf() function as follows:
measure cross-correlation
ccf(Spend, Income)
The above plot contains the correlation between the two-time series at various lags.
Obviously, numbers are more important, to get the original correlation values, we can make use of the print function.
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print cross-correlation values
print(ccf(Spend, Income))
Autocorrelations of series ‘X’, by lag -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 -0.298 -0.137 0.094 0.475 0.677 0.730 0.607 0.340 0.222 -0.040 -0.226 -0.112 -0.333 -0.228 -0.273
Inference
The cross-correlation at lag 0 is 0.340
The cross-correlation at lag 1 is 0.222
The cross-correlation at lag 2 is -0.040
And so on.
