summary_metrics

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Axis summary_metrics on sub-layer L1_5_B_univariate_summary (layer l1_5).

Sub-layer

L1_5_B_univariate_summary

Axis metadata

  • Default: ['mean', 'sd', 'min', 'max', 'n_missing']

  • Sweepable: False

  • Status: operational

Operational status summary

  • Operational: 8 option(s)

  • Future: 0 option(s)

Options

kurtosis – operational

Sample excess kurtosis per series (fourth standardised moment, normal = 0).

Adds kurtosis to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

Heavy-tail diagnostic; large values motivate winsorisation at L2.C or robust losses at L5.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_obs, n_missing, mean, sd, min, max, skew

Last reviewed 2026-05-05 by macroforecast author.

max – operational

Sample maximum per series.

Adds max to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

Detecting outlier records prior to L2.C handling; suspicious upper bounds.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_obs, n_missing, mean, sd, min, skew, kurtosis

Last reviewed 2026-05-05 by macroforecast author.

mean – operational

Sample mean per series.

Adds mean to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

First-moment summary for level series; cross-series comparison of central tendency.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_obs, n_missing, sd, min, max, skew, kurtosis

Last reviewed 2026-05-05 by macroforecast author.

min – operational

Sample minimum per series.

Adds min to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

Detecting clipping artifacts (e.g. a 0 sentinel) or suspicious lower bounds.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_obs, n_missing, mean, sd, max, skew, kurtosis

Last reviewed 2026-05-05 by macroforecast author.

n_missing – operational

Count of NaN entries per series.

Adds n_missing to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

Quantifying imputation load before L2.D runs; high counts may justify dropping the series.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_obs, mean, sd, min, max, skew, kurtosis

Last reviewed 2026-05-05 by macroforecast author.

n_obs – operational

Number of non-NaN observations per series.

Adds n_obs to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

Pair with n_missing to spot heavily-missing predictors that L2.D will need to impute.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_missing, mean, sd, min, max, skew, kurtosis

Last reviewed 2026-05-05 by macroforecast author.

sd – operational

Sample standard deviation per series.

Adds sd to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

Second-moment scale; informs whether L3 scale standardisation is necessary.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_obs, n_missing, mean, min, max, skew, kurtosis

Last reviewed 2026-05-05 by macroforecast author.

skew – operational

Sample skewness per series (third standardised moment).

Adds skew to the per-series summary table emitted by L1.5.B. summary_metrics is a multi-select axis – listing several metrics produces a wide-form table with one row per series and one column per chosen metric.

When to use

Identifying asymmetric distributions that may justify a log transform at L2.B.

References

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

  • Tukey (1977) ‘Exploratory Data Analysis’, Addison-Wesley.

Related options: n_obs, n_missing, mean, sd, min, max, kurtosis

Last reviewed 2026-05-05 by macroforecast author.