raw_outlier_policy
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Axis
raw_outlier_policyon sub-layerl1_c(layerl1).
Sub-layer
l1_c
Axis metadata
Default:
'preserve_raw_outliers'Sweepable: False
Status: operational
Operational status summary
Operational: 6 option(s)
Future: 0 option(s)
Options
preserve_raw_outliers – operational
Pass raw outliers through to L2.C.
Default; relies on L2.C McCracken-Ng IQR detection and the configured outlier_action to handle extreme values.
When to use
Default; the canonical workflow.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
Related options: winsorize_raw, iqr_clip_raw, mad_clip_raw, zscore_clip_raw, set_raw_outliers_to_missing
Last reviewed 2026-05-05 by macroforecast author.
winsorize_raw – operational
Winsorise raw series at quantile cutpoints (default p1 / p99).
Caps extreme values at the specified quantile before t-coding. Preserves observation count but compresses tails.
When to use
Heavy-tailed financial / macro series where extreme observations would dominate downstream estimates.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
Related options: preserve_raw_outliers, iqr_clip_raw
Last reviewed 2026-05-05 by macroforecast author.
iqr_clip_raw – operational
Clip raw observations beyond k×IQR thresholds.
Clips values outside Q1 - k·IQR, Q3 + k·IQR (k typically 1.5 or 3). Robust to non-Gaussian distributions.
When to use
Robust outlier handling on non-normal series.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
Related options: winsorize_raw, mad_clip_raw, zscore_clip_raw
Last reviewed 2026-05-05 by macroforecast author.
mad_clip_raw – operational
Clip raw observations beyond k×MAD thresholds.
Median Absolute Deviation -based clipping; even more robust than IQR. Default k = 3 maps to roughly 3σ for normal data.
When to use
Highly non-Gaussian series with sparse outliers.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
Related options: iqr_clip_raw, zscore_clip_raw
Last reviewed 2026-05-05 by macroforecast author.
zscore_clip_raw – operational
Clip raw observations beyond k standard deviations.
Standard z-score rule (typically k = 3). Cheapest option but assumes approximate normality.
When to use
Approximately Gaussian series; quick baseline.
When NOT to use
Heavy-tailed series – use iqr_clip_raw or mad_clip_raw.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
Related options: iqr_clip_raw, mad_clip_raw
Last reviewed 2026-05-05 by macroforecast author.
set_raw_outliers_to_missing – operational
Set raw outliers to NaN and defer to L2.D imputation.
Replaces flagged outliers with NaN. The L2.D imputation method then fills the resulting gaps; preserves observation count for downstream stages.
When to use
Pipelines where outliers should be re-imputed coherently with other missing data.
References
macroforecast design Part 1, L1: ‘data definition is the recipe layer that pins source, target, geography, and horizon – everything downstream branches off these choices.’
Related options: preserve_raw_outliers, winsorize_raw
Last reviewed 2026-05-05 by macroforecast author.