lag – Lagged target/predictor block.#

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

Function signature#

mf.functions.lag_matrix(
    panel: pd.DataFrame,
    n_lag: int,
    include_contemporaneous: bool,
) -> 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_lag

int

4

>= 1

Number of lags. Output has K * n_lag columns.

include_contemporaneous

bool

False

—

If True, also include lag 0 (the contemporaneous column).

Returns#

pd.DataFrame — scalar result.

Behavior#

Constructs a lagged matrix from inputs. params.n_lag sets the lag depth. Standard predictor for autoregressive baselines.

When to use

Always when building AR features or lagged-X feature blocks.

When NOT to use

When the target itself is already differenced/lagged in L2 – avoid double-lagging.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: lag

References#

  • macroforecast design Part 2, L3: ‘feature engineering is a DAG of typed transforms; cascade-depth bounds the longest chain at cascade_max_depth.’