seasonal_lag – Lag at a seasonal period (e.g. y_{t-12} for monthly data).#

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

Function signature#

mf.functions.seasonal_lag_matrix(
    panel: pd.DataFrame,
    seasonal_period: int,
    n_seasonal_lags: 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.

seasonal_period

int

12

>= 2

Seasonal cycle length (12 for monthly, 4 for quarterly).

n_seasonal_lags

int

1

>= 1

Number of seasonal lags. Shifts by seasonal_period * i.

Returns#

pd.DataFrame — scalar result.

Behavior#

Standard lag op restricted to the seasonal index (params.lag = 12 for monthly, 4 for quarterly). Useful for year-over-year features and seasonal AR terms.

When to use

Capturing year-over-year persistence; seasonal AR baselines.

When NOT to use

When season_dummy or X-13 deseasonalisation is preferred over lag-based seasonality.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: seasonal_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.’