`winsorize` – Cap observations at user-supplied quantile thresholds.#

Back to outlier_policy axis | Back to L2 | Browse all options

Operational op under axis outlier_policy, sub-layer l2_c, layer l2. Standalone callable: mf.functions.winsorize_clean.

Function signature#

mf.functions.winsorize_clean(
    panel: pd.DataFrame,
) -> pd.DataFrame

Parameters#

name	type	default	constraint	description
`panel`	`pd.DataFrame`	—	—	Input panel. Each column is a variable; rows are time periods. Series is promoted to a single-column DataFrame internally.

Returns#

pd.DataFrame — scalar result.

Behavior#

Truncates each series at leaf_config.winsorize_lower_quantile (default 0.01) and leaf_config.winsorize_upper_quantile (default 0.99). Less aggressive than the McCracken-Ng IQR rule and preserves more of the tail.

When to use

Studies that want a bounded but non-NaN outlier handler; alternative-rule comparisons.

In recipe context#

Set params.outlier_policy = "winsorize" in the relevant layer to activate this op within a recipe:

# Layer L2 recipe fragment
params:
  outlier_policy: winsorize

References#

macroforecast design Part 2, L2: ‘preprocessing is the only layer with a strict A→B→C→D→E execution order; every cell follows the same pipeline.’
Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

winsorize – Cap observations at user-supplied quantile thresholds.#