frame_edge_policy#
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Axis
frame_edge_policyon sub-layerl2_e(layerl2).
Sub-layer#
l2_e
Axis metadata#
Default:
'truncate_to_balanced'Sweepable: True
Status: operational
Operational status summary#
Operational: 4 option(s)
Future: 0 option(s)
Options#
truncate_to_balanced – operational#
Trim leading / trailing rows until every series is observed.
See truncate_to_balanced function page for full documentation + parameters + standalone usage. Standalone: mf.functions.truncate_to_balanced_clean.
drop_unbalanced_series – operational#
Drop predictor columns that aren’t observed across the full sample.
See drop_unbalanced_series function page for full documentation + parameters + standalone usage. Standalone: mf.functions.drop_unbalanced_series_clean.
keep_unbalanced – operational#
Keep the panel’s natural unbalanced shape.
Lets L4 estimators handle missingness directly. Required for some L4 families (LSTM/GRU/transformer) and for partial-data robustness studies.
When to use
Custom panels with intentional unbalanced structure; missing-data-robust models.
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: truncate_to_balanced, drop_unbalanced_series
Last reviewed 2026-05-04 by macroforecast author.
zero_fill_leading – operational#
Zero-fill leading missing predictor cells; preserve the rest.
See zero_fill_leading function page for full documentation + parameters + standalone usage. Standalone: mf.functions.zero_fill_leading_clean.