<jupyter_start><jupyter_text>need to add sell prices from the 'future'<jupyter_code>s = Series(sales_df, 'sales_count')
eqmodel, eqmodel_fit,  = s.model_eq()
eq_preds = eqmodel.predict_wide(s.tseries)
eq_eval = evaluate(eq_preds, s.tseries)
eq_score = eq_eval['wpl'].mean()
model, forecast, pred_quantiles = fit(s)
proph_eval = evaluate(pred_quantiles, s.tseries)
proph_score = proph_eval['wpl'].mean()
print(f'Prophet model WPL: {proph_score}')
print(f'EQ model WPL: {eq_score}')
pal = sns.color_palette('deep')
nice = pal.as_hex()
reds = ['#FFA07A', '#FA8072', '#E9967A', '#F08080', '#CD5C5C', '#DC143C', '#B22222', '#FF0000', '#8B0000', '#800000', '#FF6347', '#FF4500']
fig, ax = plt.subplots(figsize = (12,10))
model.plot(forecast, ax = ax)
for q, v in eqmodel_fit.iteritems():
    if v > 0:
        ax.axhline(eqmodel_fit[q], label = q, color = reds.pop())
ax.legend(loc = 'upper right')<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '192041', 'license': 'MIT', 'n_tokens_mistral': 417, 'n_tokens_neox': 383, 'n_words': 77, 'path': 'eda.ipynb', 'repo_name': 'chrisgschon/M5Forecasting-Uncertainty', 'size': '922'}