TimeSeriesVisualization#

class openstef_beam.analysis.visualizations.timeseries_visualization.TimeSeriesVisualization(**data: Any) → None[source]

Bases: VisualizationProvider

Creates interactive time series plots comparing forecasts with actual measurements.

Displays forecast quantiles as uncertainty bands overlaid with actual measurements on a timeline. Shows how well probabilistic forecasts capture reality over time and helps identify periods of poor performance or systematic biases.

What you’ll see:

  • Actual measurements as a line plot

  • Forecast quantiles as shaded uncertainty bands (darker = higher confidence)

  • Capacity limits as horizontal reference lines

  • Multiple model runs as different colored bands (when comparing models)

Useful for:

  • Assessing forecast accuracy across different time periods

  • Identifying when uncertainty bands fail to contain actual values

  • Spotting systematic forecast biases or seasonal patterns

  • Understanding model behavior during extreme events

Example

>>> from openstef_beam.analysis import AnalysisConfig
>>> from openstef_beam.analysis.visualizations import TimeSeriesVisualization
>>>
>>> analysis_config = AnalysisConfig(
...     visualization_providers=[
...         TimeSeriesVisualization(name="forecast_vs_actual"),
...     ]
... )
Parameters:

data (Any)

connect_gaps: bool
property supported_aggregations: set[AnalysisAggregation]

Returns the set of aggregation types supported by this provider.

Returns:

Set of supported VisualizationAggregation values.

create_by_none(report: EvaluationSubsetReport, metadata: TargetMetadata) → VisualizationOutput[source]

Creates visualization for a single target from a single run.

Generates detailed analysis for individual target performance, typically showing time series, detailed metrics, or target-specific insights.

Returns:

Visualization focused on the specific target’s performance.

Parameters:
Return type:

VisualizationOutput

create_by_run_and_none(reports: dict[RunName, list[tuple[TargetMetadata, EvaluationSubsetReport]]]) → VisualizationOutput[source]

Creates visualization comparing multiple runs on the same target group.

Groups reports by run_name and creates comparative visualizations showing how different models or configurations perform on the same targets.

Parameters:

  • reports (dict[TypeAliasType, list[tuple[TargetMetadata, EvaluationSubsetReport]]]) – Dictionary mapping run names to lists of (metadata, report) tuples for that run.

  • reports

Returns:

Visualization comparing different model runs on the same targets.

Return type:

VisualizationOutput

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str