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Bundled Estimators

The default estimator registry exposes classical temporal, spectral, geometric, wavelet, and data-driven methods. Discover the installed names with:

lrdbench list-estimators

Estimator entries in manifests use the registry name:

estimators:
  - name: Variance
    family: temporal
    target_estimand: hurst_scaling_proxy
    params:
      min_scale: 2
      max_scale: 256
      scale_ratio: 1.5
      n_bootstrap: 200

Temporal Hurst-Proxy Estimators

These estimators target hurst_scaling_proxy.

Name Method Main parameters
RS Rescaled-range log-log slope over subseries lengths. min_scale, max_scale, scale_ratio, use_anis_lloyd_correction, bootstrap parameters
DFA Detrended fluctuation analysis on the cumulative profile. min_scale, max_scale, detrend_order, bootstrap parameters
DMA Detrended moving-average fluctuation scaling. min_scale, max_scale, bootstrap parameters
AbsoluteMoment Log-log slope of aggregation level versus absolute first moment of block-aggregated series. min_scale, max_scale, scale_ratio, bootstrap parameters
Variance Log-log slope of sample variance versus block size for block-aggregated series. min_scale, max_scale, scale_ratio, bootstrap parameters
VarianceResidual Log-log slope of aggregation level versus average within-block residual variance after local detrending. min_scale, max_scale, scale_ratio, detrend_order, bootstrap parameters

The aggregation estimators map their fitted slopes onto a bounded Hurst-style proxy:

  • AbsoluteMoment: H = slope + 1
  • Variance: H = slope / 2 + 1
  • VarianceResidual: H = slope / 2

All three are approximate finite-sample methods. Interpret them with the same caution as other scale-window estimators: the block-size range, scale spacing, detrending order, contamination, and record length can materially change results.

Other Classical Estimators

Spectral estimators target long_memory_parameter:

  • GPH
  • Periodogram
  • WhittleMLE
  • ModifiedLocalWhittle

Geometric estimators target hurst_scaling_proxy:

  • Higuchi
  • GHE

Wavelet estimators target hurst_scaling_proxy:

  • WaveletOLS
  • WaveletAbryVeitch
  • WaveletBardet
  • WaveletJensen
  • WaveletWhittle

Data-Driven Estimators

The experimental supervised baselines target hurst_scaling_proxy:

  • MLRandomForest
  • MLSVR
  • MLCNN
  • MLLSTM

These require a manifest-declared ml_training block unless a model artefact path is supplied. See Data-driven estimators.

Interpretation Notes

Do not mix hurst_scaling_proxy and long_memory_parameter results in a single accuracy ranking unless the benchmark protocol explicitly justifies the comparison. For publication-facing analysis, report estimator name, family, target estimand, parameters, validity rate, and uncertainty support alongside accuracy or robustness metrics.

For maturity and failure-risk labels, see Estimator status.