linear_interpolation – Linear interpolation between adjacent observations.#

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Operational op under axis imputation_policy, sub-layer l2_d, layer l2. Standalone callable: mf.functions.linear_interpolate_clean.

Function signature#

mf.functions.linear_interpolate_clean(
    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#

Smooths over isolated missing observations. Not appropriate for leading / trailing missings.

When to use

Interior missing observations in well-behaved series.

In recipe context#

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

# Layer L2 recipe fragment
params:
  imputation_policy: linear_interpolation

References#

  • macroforecast design Part 2, L2: ‘preprocessing is the only layer with a strict A→B→C→D→E execution order; every cell follows the same pipeline.’

  • Chow & Lin (1971) ‘Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series’, RES 53(4).