# Set up the plot
fig, ax = plt.subplots(1, 1)

# Plot the actual values
ax.plot(train['ds'], train['y'], 'ko-', linewidth = 1.4, alpha = 0.8, ms = 1.8, label = 'Observations')
ax.plot(test['ds'], test['y'], 'ko-', linewidth = 1.4, alpha = 0.8, ms = 1.8, label = 'Observations')

# Plot the predicted values
ax.plot(future['ds'], future['yhat'], 'navy', linewidth = 2.4, label = 'Predicted');

# Plot the uncertainty interval as ribbon
ax.fill_between(future['ds'].dt.to_pydatetime(), future['yhat_upper'], future['yhat_lower'], alpha = 0.6,
               facecolor = 'gold', edgecolor = 'k', linewidth = 1.4, label = 'Confidence Interval')

# Put a vertical line at the start of predictions
plt.vlines(x=min(test['ds']).date(), ymin=min(future['yhat_lower']), ymax=max(future['yhat_upper']), colors = 'r',
           linestyles='dashed', label = 'Prediction Start')

# Plot formatting
plt.legend(loc = 2, prop={'size': 8}); plt.xlabel('Date'); plt.ylabel('Price $');
plt.grid(linewidth=0.6, alpha = 0.6)

plt.title('{} Model Evaluation from {} to {}.'.format(self.symbol,
    start_date.date(), end_date.date()));
plt.show();

ax.fill_between(future['ds'].dt.to_pydatetime(), future['yhat_upper'], future['yhat_lower'], alpha = 0.6,