Data models

The SDK relies on Pydantic models to validate payloads. Import them from simulacrum.models when you need structured access.

`ForecastRequest`

from simulacrum.models import ForecastRequest

ForecastRequest(
    series: list[float],
    horizon: int,
    model: str | None = "default"
)

Field	Type	Notes
`series`	`list[float]`	Automatically normalised from numpy arrays or sequences.
`horizon`	`int`	Must be positive.
`model`	`str \| None`	Optional identifier sent to the API.

Serialise with model_dump() before sending requests manually.

from simulacrum.models import ForecastResponse

ForecastResponse(forecast: list[float], model_used: str)

Field	Type	Notes
`forecast`	`list[float]`	Horizon-length values returned by the API.
`model_used`	`str`	Backend model chosen by the service.

Call get_forecast() to convert the list into a numpy array for downstream analysis.

from simulacrum.models import ValidateAPIKeyResponse

ValidateAPIKeyResponse(valid: bool, client: str, expires_at: datetime &#124; None)

Use this model to inspect key metadata. expires_at is optional and absent when the platform does not expose expiry information.