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SingleSampleForecastPayload

class SingleSampleForecastPayload(BaseModel):
    sample_id: Any
    timestamps: List[str]
    values: List[Optional[float]]
    model_name: str

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Parameters

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sample_id

Type: Any Unique identifier for the sample. Must match the input sample_id.

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timestamps

Type: List[str] List of timestamp strings for the forecasted values. Must be in ISO format or compatible with pandas datetime parsing.

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values

Type: List[Optional[float]] List of forecasted values corresponding to timestamps. None values represent missing or invalid forecasts.

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model_name

Type: str Name of the model used to generate the forecast. Must be a non-empty string.

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Raises

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ValueError

  • If timestamps or values lists are empty
  • If timestamps and values have mismatched lengths
  • If model_name is empty or whitespace

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Notes

  • NaN values in values are converted to None for JSON compatibility
  • model_name is automatically trimmed of whitespace
  • Each forecast represents predictions for one time series column

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Examples

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Basic Forecast

forecast = SingleSampleForecastPayload(
    sample_id="sales",
    timestamps=["2023-01-04", "2023-01-05"],
    values=[130.0, 140.0],
    model_name="sfm-moe-v1"
)

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Forecast with Missing Values

forecast = SingleSampleForecastPayload(
    sample_id="temperature",
    timestamps=["2023-01-04", "2023-01-05"],
    values=[23.0, None],  # Missing forecast for 2023-01-05
    model_name="sfm-moe-v1"
)

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See Also