pct_change – Period-over-period percentage change: (y_t / y_{t-1}) - 1.#

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

Function signature#

mf.functions.pct_change_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 for the percentage change.

Returns#

pd.DataFrame — scalar result.

Behavior#

Strict simple growth rate; not equivalent to log_diff for large movements. Returns NaN where the previous observation is zero or NaN.

When to use

When a literal percentage interpretation is required (returns, inflation rates).

When NOT to use

Trend-following analysis where log_diff’s symmetry is preferable.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: pct_change

References#

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