adaptive_ma_rf – AlbaMA – RF-driven adaptive moving average smoother for a single time series.#

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

Function signature#

mf.functions.adaptive_ma_rf_transform(
    panel: pd.DataFrame,
    n_estimators: int,
    min_samples_leaf: int,
    sided: str,
    random_state: int | None,
) -> 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.

n_estimators

int

100

>= 1

Number of trees in the RF ensemble. Paper recommends 500; 100 is the default for speed.

min_samples_leaf

int

40

>= 1

Minimum samples per leaf; lower-bounds the effective adaptive window length (paper default: 40).

sided

str

'two'

‘two’ or ‘one’

‘two’ fits one forest on the full sample (retrospective); ‘one’ fits an expanding-window forest per time index t (real-time variant, O(T) RF fits).

random_state

`int

None`

0

int or None

Returns#

pd.DataFrame — scalar result.

Behavior#

Goulet Coulombe & Klieber (2025) ‘Adaptive Moving Average for Macroeconomic Monitoring’ (arXiv:2501.13222 §2). A random forest fit with a single regressor – the time index – on the target series y (i.e. RF(y_t ~ t)). Per-observation leaf membership induces a weight matrix w_τt whose row sums to 1, so the smoother is a learned-bandwidth moving average of y; the realised window adapts to local volatility / regime. Paper p.8 defaults: n_estimators = B = 500, min_samples_leaf = 40, max_features = 1. sided = 'two' (default) fits one forest on the full sample (retrospective smoother); sided = 'one' fits an expanding-window forest per t (real-time nowcasting variant, paper §3.3 / p.10).

Atomic primitive: existing ma_window uses a fixed length; hamilton_filter is a regression on lags rather than a moving average; neither composes into AlbaMA without a learned window selector.

When to use

Replicating AlbaMA recipes; macro indicator monitoring under regime shifts.

When NOT to use

Multivariate denoising of a predictor panel (AlbaMA smooths a single target series).

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: adaptive_ma_rf

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 (2025) ‘An Adaptive Moving Average for Macroeconomic Monitoring’, arXiv:2501.13222.