Chaos Theory: Applications in Environmental Science: A Python Guide by Publishing Reactive & Van Der Post Hayden

Author:Publishing, Reactive & Van Der Post, Hayden
Language: eng
Format: epub
Publisher: Reactive Publishing
Published: 2024-06-07T00:00:00+00:00

# Compute and plot the autocorrelation function

plot_acf(time_series_data, lags=20)

plt.title('Autocorrelation Function')

plt.show()

```

In this example, we generate a random time series and plot its autocorrelation function using the plot_acf function from the statsmodels library. The resulting plot helps identify significant lags where past values strongly influence current values.

Partial Autocorrelation

While autocorrelation reveals the overall relationships between values at different lags, it doesn't account for the influence of intermediate values. This is where partial autocorrelation comes in. Partial autocorrelation measures the correlation between observations at different lags, excluding the effects of shorter lags.

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