saved_objects
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Axis
saved_objectson sub-layerL8_B_saved_objects(layerl8).
Sub-layer
L8_B_saved_objects
Axis metadata
Default:
NoneSweepable: False
Status: operational
Operational status summary
Operational: 20 option(s)
Future: 0 option(s)
Options
clean_panel – operational
Cleaned L2 panel (post tcode / outlier / imputation / frame edge).
The output of the L2 pipeline. Useful when downstream re-runs need to skip the (potentially expensive) cleaning stages.
When to use
When downstream re-runs without re-cleaning are needed.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, feature_metadata, forecasts, forecast_intervals
Last reviewed 2026-05-05 by macroforecast author.
combination_weights – operational
Ensemble weights from L4 combine ops.
Per-origin per-member weights produced by L4 combine ops (equal_weighted / dmsfe / inverse_msfe / mallows_cp / etc.). Active when ensemble combine ops are in the L4 DAG.
When to use
Active when ensemble combine ops are in the L4 DAG.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
decomposition – operational
L5.D decomposition tables (per-period / per-block / Shapley).
Variance / loss decomposition outputs. Default-on when L5.D decomposition is active.
When to use
Default-on when decomposition is active.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
diagnostics_all – operational
Every active diagnostic layer’s output (convenience option).
Convenience flag: enables diagnostics_l{1..4}_5 simultaneously when the corresponding diagnostic layer is active. Recommended default for first-time runs.
When to use
Default convenience option for full-diagnostic runs.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
diagnostics_l1_5 – operational
L1.5 diagnostic outputs (sample coverage / stationarity / outlier audit).
JSON + figures from the L1.5 sub-layer. Active when L1.5 is enabled in the recipe.
When to use
Active when L1.5 is enabled. Selecting diagnostics_l1_5 on l8.saved_objects activates this branch of the layer’s runtime.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
diagnostics_l2_5 – operational
L2.5 diagnostic outputs (cleaning effect summaries).
JSON + figures from the L2.5 sub-layer. Active when L2.5 is enabled.
Configures the saved_objects axis on L8_B_saved_objects (layer l8); the diagnostics_l2_5 value is materialised in the recipe’s fixed_axes block under that sub-layer.
When to use
Active when L2.5 is enabled. Selecting diagnostics_l2_5 on l8.saved_objects activates this branch of the layer’s runtime.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
diagnostics_l3_5 – operational
L3.5 diagnostic outputs (factor / lag / selection inspection).
JSON + figures from the L3.5 sub-layer. Active when L3.5 is enabled.
Configures the saved_objects axis on L8_B_saved_objects (layer l8); the diagnostics_l3_5 value is materialised in the recipe’s fixed_axes block under that sub-layer.
When to use
Active when L3.5 is enabled. Selecting diagnostics_l3_5 on l8.saved_objects activates this branch of the layer’s runtime.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
diagnostics_l4_5 – operational
L4.5 diagnostic outputs (in-sample fit / window stability / tuning history).
JSON + figures from the L4.5 sub-layer. Active when L4.5 is enabled.
Configures the saved_objects axis on L8_B_saved_objects (layer l8); the diagnostics_l4_5 value is materialised in the recipe’s fixed_axes block under that sub-layer.
When to use
Active when L4.5 is enabled. Selecting diagnostics_l4_5 on l8.saved_objects activates this branch of the layer’s runtime.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
feature_metadata – operational
L3 column lineage + pipeline definitions.
The L3 metadata sink containing per-feature lineage, transformation chain, and pipeline ID. Default-on when L7 lineage_attribution or transformation_attribution is active – those ops require this metadata to function.
When to use
Default-on when L7 lineage / transformation_attribution is active.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, forecasts, forecast_intervals
Last reviewed 2026-05-05 by macroforecast author.
forecast_intervals – operational
Per-cell prediction intervals (when forecast_object = quantile / density).
Quantile forecasts at the user-specified α levels (default 5% / 50% / 95%). Default-on when L4 emits forecast_object = quantile or density.
When to use
Default-on when forecast_object = quantile / density.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
forecasts – operational
Per-cell point forecasts.
The headline output: per (cell, target, horizon, origin) forecast. Default-on; required for replication and for every downstream L5 / L6 / L7 op.
When to use
Default-on; required for replication.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecast_intervals
Last reviewed 2026-05-05 by macroforecast author.
importance – operational
L7 importance outputs.
Tables and figures from every L7.A op in the recipe’s interpretation DAG. Default-on when L7 is enabled.
When to use
Default-on when L7 is active. Selecting importance on l8.saved_objects activates this branch of the layer’s runtime.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
metrics – operational
L5 metric tables.
Per-cell per-metric scores aggregated by the L5.C configuration. Default-on; the standard headline output for every horse-race study.
When to use
Default-on. Selecting metrics on l8.saved_objects activates this branch of the layer’s runtime.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
model_artifacts – operational
Pickled / joblib model objects.
Serialised fitted estimators (one per (model, origin) pair). Default-off because model objects can be large; enable for downstream prediction without re-fitting.
When to use
When downstream prediction without re-fitting is needed.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
ranking – operational
L5.E ranking tables.
Models ranked by primary metric / MCS inclusion / Borda count / etc. Default-on when L5.E ranking is active.
When to use
Default-on when ranking is active.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
raw_panel – operational
Raw L1 panel before any L2 cleaning.
The original raw FRED-MD / -QD / -SD / custom panel. Default-off because raw FRED panels are large; enabling this makes the run fully self-contained – a downstream user can re-run the entire pipeline from the manifest alone without internet access.
When to use
Default-off for size; enable for fully self-contained runs.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: clean_panel, feature_metadata, forecasts, forecast_intervals
Last reviewed 2026-05-05 by macroforecast author.
regime_metrics – operational
Regime-conditional metrics.
Metric breakdowns by L1.G regime classification. Default-on when L1.G regime is non-pooled (i.e. regime-conditional analysis is intended).
When to use
Active when L1.G regime is non-pooled.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
state_metrics – operational
State-level metrics for FRED-SD geographic studies.
Per-state metric breakdowns. Default-on when L1.D geography is state-level (FRED-SD pipelines).
When to use
Active when L1.D geography is state-level (FRED-SD).
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
tests – operational
L6 test outputs (DM / GW / MCS / SPA / RC / StepM / PT / residual / density).
Test statistics, p-values, kernel choices, and lag-truncation parameters for every L6 sub-layer that is enabled. Default-on when any L6 sub-layer is active.
When to use
Default-on when L6 sub-layers are active.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.
transformation_attribution – operational
L7 transformation_attribution Shapley table.
Per-pipeline Shapley contributions to forecast skill. Active when transformation_attribution is in the L7 DAG (typically alongside multi-cell sweeps over alternative L3 transforms).
When to use
Active when transformation_attribution op is in the L7 DAG.
References
macroforecast design Part 3, L8: ‘reproducibility = manifest + provenance + bit-exact replicate.’
Related options: raw_panel, clean_panel, feature_metadata, forecasts
Last reviewed 2026-05-05 by macroforecast author.