wavelet – Discrete wavelet transform – multi-scale time-frequency features.#

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

Function signature#

mf.functions.wavelet_transform(
    panel: pd.DataFrame,
    wavelet: str,
    n_levels: 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.

wavelet

str

'"db4"'

—

Wavelet family name (e.g., “db4”, “haar”). Accepted for API consistency; runtime uses a rolling-mean low-pass approximation.

n_levels

int

3

>= 1

Number of decomposition levels. Each level produces an approximation (_wA{level}) and detail (_wD{level}) pair.

Returns#

pd.DataFrame — scalar result.

Behavior#

Decomposes the series into wavelet detail and approximation coefficients at several scales (params.wavelet, params.level). Captures localised time-frequency patterns that Fourier basis cannot.

When to use

Series with localised oscillations or non-stationary cycles (financial / climate macro).

When NOT to use

Smooth seasonal patterns – use fourier instead.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: wavelet

References#

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