imputation_policy#

Back to L2 | Browse all axes | Browse all options

Axis imputation_policy on sub-layer l2_d (layer l2).

Sub-layer#

l2_d

Axis metadata#

  • Default: 'em_factor'

  • Sweepable: True

  • Status: operational

Operational status summary#

  • Operational: 6 option(s)

  • Future: 0 option(s)

Options#

em_factor – operational#

EM-factor imputation (McCracken-Ng default).

See em_factor function page for full documentation + parameters + standalone usage. Standalone: mf.functions.em_factor_impute_clean.

em_multivariate – operational#

Multivariate-Gaussian EM imputation.

See em_multivariate function page for full documentation + parameters + standalone usage. Standalone: mf.functions.em_multivariate_impute_clean.

mean – operational#

Replace missing cells with the per-series rolling mean.

See mean function page for full documentation + parameters + standalone usage. Standalone: mf.functions.mean_impute_clean.

forward_fill – operational#

Carry the last observed value forward.

See forward_fill function page for full documentation + parameters + standalone usage. Standalone: mf.functions.forward_fill_clean.

linear_interpolation – operational#

Linear interpolation between adjacent observations.

See linear_interpolation function page for full documentation + parameters + standalone usage. Standalone: mf.functions.linear_interpolate_clean.

none_propagate – operational#

Pass NaN through; downstream layers handle it.

Useful when the recipe wants L3 / L4 to see the missing pattern (e.g., for missingness-as-feature studies).

When to use

Studies that treat missingness as informative; or panels with no missings.

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.’

Related options: em_factor, mean, forward_fill

Last reviewed 2026-05-04 by macroforecast author.