`diff` – First difference: `y_t - y_{t-1}`.#

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

Function signature#

mf.functions.diff_transform(
    panel: pd.DataFrame,
    periods: 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.
`periods`	`int`	`1`	>= 1	Number of lag periods to difference.

Returns#

pd.DataFrame — scalar result.

Behavior#

Computes the simple first difference on the input column. The first observation becomes NaN. Combine with lag to recover level features when the L2 layer already differenced the panel.

When to use

I(1) variables that need a stationary representation in addition to the L2-applied tcode.

When NOT to use

When the panel is already differenced by L2.B (avoids double-differencing).

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: diff

References#

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

diff – First difference: y_t - y_{t-1}.#