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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).
An example autocorrelation plot. Sharp peaks indicate a sharp correlation in time series, whereas shorter peaks indicate little correlation in the time series.
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 series fig = tsaplots.plot_acf(co2_levels['co2'], lags=24) plt.show()
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