training_start_rule

Back to L4 | Browse all axes | Browse all options

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.