multitask.factories.predict_quantile_band

multitask.factories.predict_quantile_band(
    forecasters,
    steps,
    *,
    last_window=None,
    exog=None,
    enforce_monotonic=True,
)

Assemble per-quantile forecasts into one non-crossing band.

Calls predict on each fitted forecaster from :func:quantile_lgbm_forecaster_factory and stacks the results into columns named q_<level> (e.g. q_0.1), in ascending quantile order. Because the quantile heads are fitted independently they can cross (a higher quantile predicting below a lower one); with enforce_monotonic the rows are sorted ascending (the Chernozhukov rearrangement), a deterministic post-hoc fix that restores monotonicity without changing the marginal quantile levels.

Parameters

Name	Type	Description	Default
forecasters	Mapping[float, `ForecasterRecursive`]	Map from quantile level to a fitted `ForecasterRecursive` (as returned by :func:`quantile_lgbm_forecaster_factory`, after fit).	required
steps	int	Forecast horizon passed to each `predict`.	required
last_window	Optional[Any]	Optional last-window override forwarded to `predict`.	`None`
exog	Optional[Any]	Optional exogenous frame forwarded to `predict`.	`None`
enforce_monotonic	bool	When `True` (default), sort each row ascending so the band never crosses.	`True`

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: One column per quantile (`q_<level>`), indexed by the
	pd.DataFrame	forecast horizon.

Raises

Name	Type	Description
	ValueError	If forecasters is empty or its levels are invalid.

Examples

import numpy as np
import pandas as pd
import types
from spotforecast2_safe.multitask.factories import (
    quantile_lgbm_forecaster_factory,
    predict_quantile_band,
)

idx = pd.date_range("2023-01-01", periods=300, freq="h")
y = pd.Series(
    50 + 10 * np.sin(np.arange(300) * 2 * np.pi / 24), index=idx, name="y"
)
config = types.SimpleNamespace(
    random_state=0, lags_consider=[1, 24], window_size=24
)
heads = quantile_lgbm_forecaster_factory(config, quantiles=[0.1, 0.5, 0.9])
for fc in heads.values():
    fc.fit(y=y)
band = predict_quantile_band(heads, steps=6)
print(band.columns.tolist())
# Non-crossing after rearrangement.
assert (band["q_0.1"] <= band["q_0.5"] + 1e-9).all()
assert (band["q_0.5"] <= band["q_0.9"] + 1e-9).all()

╭─────────────────────────────── IgnoredArgumentWarning ───────────────────────────────╮
│ The number of bins has been reduced from 10 to 4 due to duplicated edges caused by   │
│ repeated predicted values.                                                           │
│                                                                                      │
│ Category : spotforecast2.exceptions.IgnoredArgumentWarning                           │
│ Location :                                                                           │
│ /home/runner/work/spotforecast2-safe/spotforecast2-safe/src/spotforecast2_safe/prepr │
│ ocessing/_binner.py:259                                                              │
│ Suppress : warnings.simplefilter('ignore', category=IgnoredArgumentWarning)          │
╰──────────────────────────────────────────────────────────────────────────────────────╯

['q_0.1', 'q_0.5', 'q_0.9']