Enforce prior belief-time constraint on forecasting pipeline input data

[Copilot]

The prior parameter was only tagging saved forecasts with the specified belief time (save_belief_time) but was not actually filtering which beliefs were loaded as training/prediction input. Only future-regressor beliefs were partially filtered in split_data_all_beliefs; the target sensor and past regressors used the most recent available belief regardless of prior.

Changes

Implementation

• ForecasterParametersSchema.resolve_config: Exposes beliefs_before=data.get("belief_time") in the returned params dict — None when prior is absent, the explicit prior value otherwise.
• BasePipeline: Adds beliefs_before parameter; passes it to sensor.search_beliefs() in load_data_all_beliefs(), restricting all sensor inputs (target, past regressors, future regressors) to beliefs recorded at or before prior.
• TrainPipeline / PredictPipeline: Thread beliefs_before through to BasePipeline.
• TrainPredictPipeline.run_cycle: Passes self._parameters.get("beliefs_before") to both sub-pipelines.

Test

• New fixture setup_fresh_test_forecast_data_with_anomalous_beliefs: creates a sensor with two belief layers for the same events:
  • Phase 1 — normal values (50 kW), belief_time = 2024-12-31
  • Phase 2 — anomalous values (5000 kW), belief_time = 2025-01-10
• test_prior_restricts_training_beliefs: runs the pipeline twice — once with prior before Jan 10 (model sees only normal data → low forecasts) and once with prior after Jan 10 (model sees anomalous data → high forecasts) — asserting the two outcomes differ by at least 10×.

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