`time_trend` – Deterministic linear time trend (`t = 1, 2, ...`).#

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.time_trend_transform.

Function signature#

mf.functions.time_trend_transform(
    panel: pd.DataFrame,
) -> 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.

Returns#

pd.DataFrame — scalar result.

Behavior#

Adds a column time_trend to the panel; with params.degree > 1 appends polynomial trends. Deterministic complement to stochastic detrending (HP / Hamilton).

When to use

Trend-stationary linear models where a deterministic trend is part of the DGP.

When NOT to use

Series with structural breaks – use regime_indicator or stochastic detrending instead.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: time_trend

References#

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

time_trend – Deterministic linear time trend (t = 1, 2, ...).#