Taxing Exercise: Compute the ACF

In the last chapter, you computed autocorrelations with one lag. Often we are interested in seeing the autocorrelation over many lags. The quarterly earnings for H&R Block (ticker symbol HRB) is plotted, and you can see the extreme cyclicality of its earnings. A vast majority of its earnings occurs in the quarter that taxes are due.

You will compute the array of autocorrelations for the H&R Block quarterly earnings that is pre-loaded in the DataFrame HRB. Then, plot the autocorrelation function using the plot_acf module. This plot shows what the autocorrelation function looks like for cyclical earnings data. The ACF at lag=0 is always one, of course. In the next exercise, you will learn about the confidence interval for the ACF, but for now, suppress the confidence interval by setting alpha=1.

This exercise is part of the course

Time Series Analysis in Python

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Exercise instructions

Import the acf module and plot_acf module from statsmodels.
Compute the array of autocorrelations of the quarterly earnings data in DataFrame HRB.
Plot the autocorrelation function of the quarterly earnings data in HRB, and pass the argument alpha=1 to suppress the confidence interval.

Hands-on interactive exercise

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

# Import the acf module and the plot_acf module from statsmodels
from statsmodels.tsa.stattools import acf
from statsmodels.graphics.tsaplots import plot_acf

# Compute the acf array of HRB
acf_array = acf(___)
print(acf_array)

# Plot the acf function
plot_acf(___)
plt.show()

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