Add option to clip forecast based on context values#502
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shchur
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May 15, 2026
| assert context_minmax is not None | ||
| clamp_min = context_minmax[0] - clip_factor * loc_scale[1] | ||
| clamp_max = context_minmax[1] + clip_factor * loc_scale[1] | ||
| quantile_preds = quantile_preds.clamp(min=clamp_min, max=clamp_max) |
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Will this PR also take care of the issue of exploding predictions for all-NaN context?
import numpy as np
from chronos import Chronos2Pipeline
model = Chronos2Pipeline.from_pretrained("amazon/chronos-2")
model.predict(np.full((1, 1, 50), float("nan")), prediction_length=5)
# [tensor([[[-8.6131e+06, -1.0066e+07, -1.0386e+07, -1.0977e+07, -1.1329e+07],
# [-1.2380e+05, -1.1830e+05, -1.5210e+05, -1.5608e+05, -1.5930e+05],
# [-3.2544e+04, -2.7985e+04, -3.9638e+04, -4.4504e+04, -5.1272e+04],
# ...I have a hunch that this is also somehow related to the scale computation.
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