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Add validate_inputs to predict_df in BaseChronosPipeline #427

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Merged
abdulfatir merged 1 commit into from
Dec 8, 2025
Merged

Add validate_inputs to predict_df in BaseChronosPipeline #427

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4 changes: 4 additions & 0 deletions src/chronos/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,7 @@ def predict_df(
target: str = "target",
prediction_length: int | None = None,
quantile_levels: list[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
validate_inputs: bool = True,
**predict_kwargs,
) -> "pd.DataFrame":
"""
Expand All @@ -162,6 +163,8 @@ def predict_df(
Number of steps to predict for each time series
quantile_levels
Quantile levels to compute
validate_inputs
When True, the dataframe(s) will be validated before prediction
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@shchur shchur Dec 8, 2025

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Maybe mention what exactly is validated?

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When True, the dataframe(s) will be validated before prediction
When True, the dataframe(s) will be validated before prediction, ensuring that timestamps have a regular frequency, and item IDs match between past and future data. Setting to False disables these checks.

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@shchur shchur Dec 8, 2025

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This is optional - feel free to ignore

**predict_kwargs
Additional arguments passed to predict_quantiles

Expand Down Expand Up @@ -196,6 +199,7 @@ def predict_df(
timestamp_column=timestamp_column,
target_columns=[target],
prediction_length=prediction_length,
validate_inputs=validate_inputs,
)

# NOTE: any covariates, if present, are ignored here
Expand Down