`scaled_pca` – Scaled / weighted PCA (target-aware factor extraction).#

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Operational op under axis op, sub-layer L3_A_step_op, layer l3. Standalone callable: mf.functions.scaled_pca_transform.

Function signature#

mf.functions.scaled_pca_transform(
    panel: pd.DataFrame,
    target: pd.Series,
    n_components: int,
) -> 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.
`target`	`pd.Series`	—	—	Supervisory signal aligned to the panel index. Must share at least one index value with panel; raises ValueError if the intersection is empty.
`n_components`	`int`	`3`	>= 1	Number of principal components to extract. Clamped internally to min(T_clean, K) - 1.

Returns#

pd.DataFrame — scalar result.

Behavior#

Weights each column by its predictive correlation with the target before performing PCA. Implements the Huang-Jiang-Tu-Zhou (2022) scaled PCA for forecasting macro variables.

Reduces to plain PCA when all weights are equal.

When to use

When standard PCA’s leading factor is dominated by predictively-irrelevant variance.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: scaled_pca

References#

macroforecast design Part 2, L3: ‘feature engineering is a DAG of typed transforms; cascade-depth bounds the longest chain at cascade_max_depth.’
Huang, Jiang, Tu & Zhou (2022) ‘Scaled PCA: A New Approach to Dimension Reduction’, Management Science 68(3): 1678-1695.

scaled_pca – Scaled / weighted PCA (target-aware factor extraction).#