Autocorrelation measures a set of current values against a set of past values to see if they correlate. It is heavily used in time series analysis and forecasting.
We can calculate the correlation for current time-series observations with observations of previous time steps called lags.
A plot of the autocorrelation of a time series is called the Autocorrelation Function (ACF).
Autocorrelation plots graphically summarize the relationship between an observation in a time series and an observation at a prior time.
The code to graphically visualize the Autocorrelation of data is given below. The plot_acf
function takes in two inputs: the data column and the value for lag.
from statsmodels.graphics import tsaplots# Display the autocorrelation plot of your time seriesfig = tsaplots.plot_acf(co2_levels['co2'], lags=24)plt.show()