savitzky_golay_filter – Polynomial-fit smoothing filter (Savitzky & Golay 1964).#

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

Function signature#

mf.functions.savitzky_golay_transform(
    panel: pd.DataFrame,
    window: int,
    polyorder: 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.

window

int

7

>= 3

Length of the smoothing window. If even, rounded up to next odd integer (scipy requirement).

polyorder

int

3

—

Degree of the polynomial fit within each window. Must be < window.

Returns#

pd.DataFrame — scalar result.

Behavior#

Local polynomial regression smoothing: each output value is the polynomial-fit centre value of a moving window. window_length (default 5) and polyorder (default 2) parameterise the kernel. Operational: runtime delegates to scipy.signal.savgol_filter (scipy is a hard dependency).

Used as the fixed-window baseline against which Goulet Coulombe & Klieber (2025) AlbaMA’s adaptive-window estimator is compared in the v0.9.x replication recipe.

When to use

Smoothing macro indicator series for monitoring; AlbaMA replication baseline.

When NOT to use

Series with strong non-linear trends – the polynomial fit smooths them out.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: savitzky_golay_filter

References#

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

  • Savitzky & Golay (1964) ‘Smoothing and Differentiation of Data by Simplified Least Squares Procedures’, Analytical Chemistry 36(8).