season_dummy – Calendar dummy variables (month-of-year, quarter-of-year).#

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.season_dummy_transform.

Function signature#

mf.functions.season_dummy_transform(
    panel: pd.DataFrame,
    season: str enum {"quarter", "month"},
) -> 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.

season

str enum {"quarter", "month"}

'"quarter"'

—

Seasonal granularity hint. Accepted values: “quarter” and “month”. Currently validated but has no effect on output (deprecated – kept for API compatibility). Non-DatetimeIndex inputs produce season_* columns; DatetimeIndex inputs produce month_* columns.

Returns#

pd.DataFrame — scalar result.

Behavior#

Generates params.n - 1 0/1 indicators for the calendar period (drops one to avoid multicollinearity with intercept). Standard frequentist seasonality control.

When to use

Capturing calendar seasonality in linear models when a smooth Fourier basis would over-shrink discrete jumps.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: season_dummy

References#

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