window_view

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Axis window_view on sub-layer L4_5_C_window_stability (layer l4_5).

Sub-layer

L4_5_C_window_stability

Axis metadata

  • Default: 'multi'

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 5 option(s)

  • Future: 0 option(s)

Options

first_vs_last_window_forecast – operational

First vs last training-window forecast overlay.

L4.5.C window view first_vs_last_window_forecast.

This option configures the window_view axis on the L4_5_C_window_stability sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_C_window_stability/ alongside the other selected views.

When to use

Quick window-instability check; large divergence flags non-stationarity.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: rolling_train_loss, parameter_stability, rolling_coef, multi

Last reviewed 2026-05-05 by macroforecast author.

multi – operational

Render every window-stability view.

L4.5.C window view multi.

This option configures the window_view axis on the L4_5_C_window_stability sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_C_window_stability/ alongside the other selected views.

When to use

Comprehensive stability audit.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: rolling_train_loss, parameter_stability, rolling_coef, first_vs_last_window_forecast

Last reviewed 2026-05-05 by macroforecast author.

parameter_stability – operational

Parameter (coefficient / depth) stability across windows.

L4.5.C window view parameter_stability.

This option configures the window_view axis on the L4_5_C_window_stability sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_C_window_stability/ alongside the other selected views.

When to use

Spotting structural instability in the fitted estimator.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: rolling_train_loss, rolling_coef, first_vs_last_window_forecast, multi

Last reviewed 2026-05-05 by macroforecast author.

rolling_coef – operational

Coefficient values across rolling windows.

L4.5.C window view rolling_coef.

This option configures the window_view axis on the L4_5_C_window_stability sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_C_window_stability/ alongside the other selected views.

When to use

Linear-model coefficient drift detection; pair with the L7 mrf_gtvp for non-linear analogue.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: rolling_train_loss, parameter_stability, first_vs_last_window_forecast, multi

Last reviewed 2026-05-05 by macroforecast author.

rolling_train_loss – operational

Training loss across rolling windows.

L4.5.C window view rolling_train_loss.

This option configures the window_view axis on the L4_5_C_window_stability sub-layer of L4.5; output is emitted under manifest.diagnostics/l4_5/L4_5_C_window_stability/ alongside the other selected views.

When to use

Detecting training instability; rising loss across windows flags drift.

References

  • macroforecast design Part 4: ‘diagnostic layers default-off; non-blocking; produce JSON + matplotlib views attached to manifest.diagnostics/.’

Related options: parameter_stability, rolling_coef, first_vs_last_window_forecast, multi

Last reviewed 2026-05-05 by macroforecast author.