`pca` – Principal component analysis – linear factor extraction.#

Back to op axis | Back to L3 | Browse all options

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

Function signature#

mf.functions.pca_transform(
    panel: pd.DataFrame,
    n_components: int | str,
) -> 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.
`n_components`	`int	str`	`3`	>= 1 or ‘all’

Returns#

pd.DataFrame — scalar result.

Behavior#

Eigendecomposition of the column covariance; returns the top params.n_components principal components. Implements the Stock-Watson (2002) diffusion-index workflow used throughout FRED-MD applications.

Combine with factor_augmented_ar or factor_augmented_var at L4 to build the diffusion-index forecaster. temporal_rule controls whether components are re-fit per origin (default: expanding_window_per_origin).

When to use

Reducing FRED-MD’s 100+ predictors to a handful of latent factors; factor-augmented forecasts.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: 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.’
Stock & Watson (2002) ‘Forecasting Using Principal Components from a Large Number of Predictors’, JASA 97(460): 1167-1179.

pca – Principal component analysis – linear factor extraction.#