TIME-SERIES WEATHER: FORECASTING AND PREDICTION WITH PYTHON by Vivian Siahaan Rismon Hasiholan Sianipar

Author:Vivian Siahaan, Rismon Hasiholan Sianipar
Language: eng
Format: epub
Tags: Balige Publishing
Publisher: BALIGE PUBLISHING

The summary provides information about the estimated coefficients, their statistical significance, and other model diagnostics. It helps in assessing the model's performance, identifying important features, and interpreting the results.

Step 4

Plots residual error and its density:

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#Plots residual errors and its density

residuals = pd.DataFrame(model_fit.resid)

fig, ax = plt.subplots(1,2)

residuals.plot(linewidth=10, ax=ax[0])

ax[0].set_title("Residuals", fontsize=40)

residuals.plot(kind='kde', title='Density',
linewidth=10, ax=ax[1])

ax[1].set_title("Density", fontsize=40)

plt.show()

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