Autocorrelation in time series data
In the field of time series analysis, autocorrelation refers to the correlation of a time series with a lagged version of itself. For example, an autocorrelation of order 3
returns the correlation between a time series and its own values lagged by 3 time points.
It is common to use the autocorrelation (ACF) plot, also known as self-autocorrelation, to visualize the autocorrelation of a time-series. The plot_acf()
function in the statsmodels
library can be used to measure and plot the autocorrelation of a time series.
This is a part of the course
“Visualizing Time Series Data in Python”
Exercise instructions
- Import
tsaplots
fromstatsmodels.graphics
. - Use the
plot_acf()
function fromtsaplots
to plot the autocorrelation of the'co2'
column inco2_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')
from ____ import ____
# Display the autocorrelation plot of your time series
fig = ____(co2_levels[____], lags=____)
# Show plot
plt.show()