ResidualsOverTime
Residual against observation index, with the zero line.
The residual plot used by regression scatters residuals against the predicted value; forecasting keeps that diagnostic but plots against time, because time is where the error pattern lives. A band that widens to the right means the forecast error compounds over the horizon, a band that drifts off zero means the model is systematically behind or ahead of the series, and a repeating pattern means a missed seasonality.
Methods
compute(self, y_true, y_pred, class_names: Optional[List[str]] = None) -> List[DashAI.back.core.artifacts.Artifact]
ResidualsOverTimeBuild the residuals against observation index scatter.
Parameters
- y_true : ndarray
- Ground truth values of the series.
- y_pred : ndarray
- The model's forecast for the same points.
- class_names : Optional[List[str]]
- Unused; always None for forecasting.
Returns
- List[Artifact]
- A single scatter figure with the zero reference line.
get_credential(self, name: str)
ConfigObjectResolve a registered credential component by name.
Parameters
- name : str
- Credential component class name (e.g. "HuggingFaceCredential").
Returns
- BaseCredential
- An instance of the requested credential component.
get_metadata(cls) -> Dict[str, Any]
BaseReportGet metadata values for the current report.
Returns
- Dict[str, Any]
- UI metadata, including whether the report needs a model that outputs class probabilities.
get_schema(cls) -> dict
ConfigObjectGenerates the component related Json Schema.
Returns
- dict
- Dictionary representing the Json Schema of the component.
validate_and_transform(self, raw_data: dict) -> dict
ConfigObjectIt takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.
Parameters
- raw_data : dict
- A dictionary with the data provided by the user to initialize the model.
Returns
- dict
- A validated dictionary with the necessary objects.