principal_component_regression – Principal component regression (PCA → OLS).#

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Operational op under axis family, sub-layer L4_A_model_selection, layer l4. Standalone callable: mf.functions.pcr_fit.

Function signature#

mf.functions.pcr_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
) -> PCRFitResult

Parameters#

name

type

default

constraint

description

X

`np.ndarray

pd.DataFrame`

—

—

y

`np.ndarray

pd.Series`

—

—

Returns#

PCRFitResult — frozen dataclass with fit results.

Attribute

Type

Description

.n_components

int

Number of principal components used in regression.

.predict(X)

np.ndarray

Predictions for new data X, shape (n_samples,).

.summary()

str

Table: component count.

Behavior#

Identical to factor_augmented_ar without the AR lags. Useful when the target’s own lags add noise (rare but happens for highly seasonal series).

When to use

Diffusion-index forecasts where AR augmentation hurts performance.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: principal_component_regression

References#

  • macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’