asymmetric_trim – Albacore-family rank-space transformation (Goulet Coulombe et al. 2024).#

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

Function signature#

mf.functions.asymmetric_trim_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#

Per-period sort: panel Π of shape (T, K) is mapped to O where O[t, r] = sort(Π[t, :])[r] (ascending). Asymmetric trimming emerges in the downstream nonneg ridge (ridge(coefficient_constraint=nonneg)) that learns rank-position weights – this op does the rank-space transformation only.

Optional smooth_window > 0 applies a centred moving average to each rank-position time series (paper §3 mentions 3-month MA for noisy components; users can chain ma_window explicitly when they want a different window).

Operational from v0.8.9 (B-6). Layer scope (l2, l3) so the L3 DAG can dispatch it at recipe time. Algorithm spec: docs/replications/maximally_forward_looking_algorithm_notes.md.

When to use

Building Albacore_ranks-style core inflation indicators; supervised asymmetric trimming where the band is learned from data.

When NOT to use

Symmetric trimmed-mean targets (use a fixed-window ma_window instead).

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: asymmetric_trim

References#

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

  • Goulet Coulombe, Klieber, Barrette & Goebel (2024) ‘Maximally Forward-Looking Core Inflation’, technical report (R package: assemblage).