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Mean absolute error

Obviously, before you use the model to predict, you want to know how accurate your predictions are. The mean absolute error (MAE) is a good statistic for this. It is the mean difference between your predictions and the true values.

In this exercise you will calculate the MAE for an ARMA(1,1) model fit to the earthquakes time series

numpy has been imported into your environment as np and the earthquakes time series is available for you as earthquake.

Diese Übung ist Teil des Kurses

ARIMA Models in Python

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Anleitung zur Übung

  • Use np functions to calculate the Mean Absolute Error (MAE) of the .resid attribute of the results object.
  • Print the MAE.
  • Use the DataFrame's .plot() method with no arguments to plot the earthquake time series.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Fit model
model = ARIMA(earthquake, order=(1,0,1))
results = model.fit()

# Calculate the mean absolute error from residuals
mae = ____

# Print mean absolute error
print(____)

# Make plot of time series for comparison
____
plt.show()
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