Partial autocorrelation in time series data

Like autocorrelation, the partial autocorrelation function (PACF) measures the correlation coefficient between a time-series and lagged versions of itself. However, it extends upon this idea by also removing the effect of previous time points. For example, a partial autocorrelation function of order 3 returns the correlation between our time series (t_1, t_2, t_3, …) and its own values lagged by 3 time points (t_4, t_5, t_6, …), but only after removing all effects attributable to lags 1 and 2.

The plot_pacf() function in the statsmodels library can be used to measure and plot the partial autocorrelation of a time series.

This is a part of the course

“Visualizing Time Series Data in Python”

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Exercise instructions

Import tsaplots from statsmodels.graphics.
Use the plot_pacf() function from tsaplots to plot the partial autocorrelation of the 'co2' column in co2_levels.
Specify a maximum lag of 24.

Hands-on interactive exercise

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

# Import required libraries
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
____

# Display the partial autocorrelation plot of your time series
fig = ____(co2_levels[____], lags=____)

# Show plot
plt.show()

Edit and Run Code