missing_view

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Axis missing_view on sub-layer L1_5_D_missing_outlier_audit (layer l1_5).

Sub-layer

L1_5_D_missing_outlier_audit

Axis metadata

  • Default: 'multi'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 4 option(s)

  • Future: 0 option(s)

Options

heatmap – operational

Visualisation of missing pattern over time.

L1.5.D missing-data visualisation heatmap.

This option configures the missing_view axis on the L1_5_D_missing_outlier_audit sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_D_missing_outlier_audit/ alongside the other selected views.

When to use

Detecting block-missingness (e.g. all 1980s missing) vs scattered NaNs that influences the choice of imputation method.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: per_series_count, longest_gap, multi

Last reviewed 2026-05-05 by macroforecast author.

longest_gap – operational

Maximum consecutive-missing run per series.

L1.5.D missing-data visualisation longest_gap.

This option configures the missing_view axis on the L1_5_D_missing_outlier_audit sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_D_missing_outlier_audit/ alongside the other selected views.

When to use

Critical for forward-fill imputation – long runs distort the imputed values; values > 12 (monthly) typically rule out forward-fill.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: per_series_count, heatmap, multi

Last reviewed 2026-05-05 by macroforecast author.

multi – operational

Produce all three missingness views.

L1.5.D missing-data visualisation multi.

This option configures the missing_view axis on the L1_5_D_missing_outlier_audit sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_D_missing_outlier_audit/ alongside the other selected views.

When to use

Comprehensive missingness audit; recommended default for first-time runs.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: per_series_count, heatmap, longest_gap

Last reviewed 2026-05-05 by macroforecast author.

per_series_count – operational

Bar chart of NaN count per series.

L1.5.D missing-data visualisation per_series_count.

This option configures the missing_view axis on the L1_5_D_missing_outlier_audit sub-layer of L1.5; output is emitted under manifest.diagnostics/l1_5/L1_5_D_missing_outlier_audit/ alongside the other selected views.

When to use

Quick view of where L2.D imputation will work hardest; outliers in this chart flag candidates for dropping.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: heatmap, longest_gap, multi

Last reviewed 2026-05-05 by macroforecast author.