training_start_rule#

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Axis training_start_rule on sub-layer L4_C_training_window (layer l4).

Sub-layer#

L4_C_training_window

Axis metadata#

  • Default: 'expanding'

  • Sweepable: True

  • Status: operational

Operational status summary#

  • Operational: 3 option(s)

  • Future: 0 option(s)

Options#

expanding – operational#

Expanding window: training data grows by one observation per origin.

Standard pseudo-OOS protocol. Each origin sees all data from t=0 up to that origin.

When to use

Default. Comparable across publications.

References

  • macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

Last reviewed 2026-05-04 by macroforecast author.

rolling – operational#

Rolling window of fixed size (params.rolling_window).

Drops early observations; useful for non-stationary series where parameter drift matters.

When to use

Non-stationary series; structural-change studies.

References

  • macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

Last reviewed 2026-05-04 by macroforecast author.

fixed – operational#

Fixed window with start/end pinned in leaf_config.

Useful for ablation studies where every origin should see the same training sample.

When to use

Replication of papers with fixed training windows.

References

  • macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’

Last reviewed 2026-05-04 by macroforecast author.