Getting "Warmed" Up: Look at Autocorrelations

Since the temperature series, temp_NY, is a random walk with drift, take first differences to make it stationary. Then compute the sample ACF and PACF. This will provide some guidance on the order of the model.

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Time Series Analysis in Python

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Anleitung zur Übung

Import the modules for plotting the sample ACF and PACF
Take first differences of the DataFrame temp_NY using the pandas method .diff()
Create two subplots for plotting the ACF and PACF
- Plot the sample ACF of the differenced series
- Plot the sample PACF of the differenced series

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Import the modules for plotting the sample ACF and PACF
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf

# Take first difference of the temperature Series
chg_temp = ___.___
chg_temp = chg_temp.dropna()

# Plot the ACF and PACF on the same page
fig, axes = plt.subplots(2,1)

# Plot the ACF
plot_acf(___, lags=20, ax=axes[0])

# Plot the PACF
plot_pacf(___, lags=20, ax=axes[1])
plt.show()

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