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
Exercise instructions
- Import the
acf
module andplot_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 argumentalpha=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()