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plotting

plot_backtests(y_true, y_preds, n_cols=2, last_n=DEFAULT_LAST_N, **kwargs)

Given panel DataFrame of observed values y and backtests across splits y_pred, returns subplots for each individual entity / time-series.

Note: if you have over 10 entities / time-series, we recommend using the rank_ functions in functime.evaluation then df.head() before plotting.

Parameters:

Name Type Description Default
y_true DataFrame

Panel DataFrame of observed values.

required
y_preds DataFrame

Panel DataFrame of backtested values.

required
n_cols int

Number of columns to arrange subplots. Defaults to 2.

2
last_n int

Plot last_n most recent values in y and y_pred. Defaults to 64.

DEFAULT_LAST_N

Returns:

Name Type Description
figure Figure

Plotly subplots.

plot_comet(y_train, y_test, y_pred, scoring=None, **kwargs)

Given a train-test-split of panel data (y_train, y_test) and forecast y_pred, returns a Comet plot i.e. scatterplot of volatility per entity in y_train against the forecast scores.

Parameters:

Name Type Description Default
y_train DataFrame

Panel DataFrame of train dataset.

required
y_test DataFrame

Panel DataFrame of test dataset.

required
y_pred DataFrame

Panel DataFrame of forecasted values to score against y_test.

required
scoring Optional[metric]

If None, defaults to SMAPE.

None

Returns:

Name Type Description
figure Figure

Plotly scatterplot.

plot_forecasts(y_true, y_pred, n_cols=2, last_n=DEFAULT_LAST_N, **kwargs)

Given panel DataFrames of observed values y and forecasts y_pred, returns subplots for each individual entity / time-series.

Note: if you have over 10 entities / time-series, we recommend using the rank_ functions in functime.evaluation then df.head() before plotting.

Parameters:

Name Type Description Default
y_true DataFrame

Panel DataFrame of observed values.

required
y_pred DataFrame

Panel DataFrame of forecasted values.

required
n_cols int

Number of columns to arrange subplots. Defaults to 2.

2
last_n int

Plot last_n most recent values in y and y_pred. Defaults to 64.

DEFAULT_LAST_N

Returns:

Name Type Description
figure Figure

Plotly subplots.

plot_fva(y_true, y_pred, y_pred_bench, scoring=None, **kwargs)

Given two panel data forecasts y_pred and y_pred_bench, returns scatterplot of benchmark scores against forecast scores. Each dot represents a single entity / time-series.

Parameters:

Name Type Description Default
y_true DataFrame

Panel DataFrame of test dataset.

required
y_pred DataFrame

Panel DataFrame of forecasted values.

required
y_pred_bench DataFrame

Panel DataFrame of benchmark forecast values.

required
scoring Optional[metric]

If None, defaults to SMAPE.

None

Returns:

Name Type Description
figure Figure

Plotly scatterplot.

plot_residuals(y_resids, n_bins=None, **kwargs)

Given panel DataFrame of residuals across splits y_resids, returns binned counts plot of forecast residuals colored by entity / time-series.

Useful for residuals analysis (bias and normality) at scale.

Parameters:

Name Type Description Default
y_resids DataFrame

Panel DataFrame of forecast residuals (i.e. observed less forecast).

required
n_bins int

Number of bins.

None

Returns:

Name Type Description
figure Figure

Plotly histogram.