Visualizing forecast results
After defining and training models using backtesting, it's time to visualize the results. Visualization is a quick and effective way to assess model performance across partitions.
The ts and bkt_df DataFrames from previous exercises, along with the Plotly library, have already been preloaded for you. Let's explore how well our models performed!
Cet exercice fait partie du cours
Designing Forecasting Pipelines for Production
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
partitions_labels = bkt_df["cutoff"].unique()
ts_sub = ts[ts["ds"] > ts["ds"].max() - datetime.timedelta(hours=24 * 7)]
# Create subplots with four rows (one for each partition)
fig = make_subplots(rows=4, cols=1, subplot_titles=["Partitions: " + str(i) for i in partitions_labels])
r = 1
for i in partitions_labels:
if r == 1:
showlegend = True
else:
showlegend = False
bkt_sub = bkt_df[bkt_df["cutoff"] == i]
# Add actual values to the plot
fig.append_trace(go.Scatter(x=ts_sub["ds"], y=ts_sub["y"], legendgroup="actual", showlegend=showlegend,
mode='lines', name='Actual', line=dict(color='#023047', width=2)), row=r, col=1)
# Add k-nearest neighbors predictions
fig.append_trace(go.Scatter(x=bkt_sub["ds"], y=bkt_sub["knn"], mode='lines', name='k-nearest neighbors',
legendgroup="knn", showlegend=showlegend, line=dict(color='#2a9d8f', width=1.5, dash="dash")), row=r, col=1)
# Add Multi-layer Perceptron predictions
fig.append_trace(go.Scatter(x=bkt_sub["ds"], y=bkt_sub["mlp"], mode='lines', name='Multi-layer Perceptron',
legendgroup="mlp", showlegend=showlegend, line=dict(color='#0077b6', width=1.5, dash="dot")), row=r, col=1)
# Add ElasticNet predictions
fig.append_trace(go.Scatter(x=bkt_sub["ds"], y=bkt_sub["enet"], mode='lines', name='ElasticNet',
legendgroup="enet", showlegend=showlegend, line=dict(color='#ffc8dd', width=1.5, dash="dot")), row=r, col=1)
r = r + 1
fig.update_layout(height=500)
fig.show()