lrdbench 1.2.1

A reproducible benchmark framework for evaluating long-range dependence estimators on canonical, contaminated, and observational time series.

lrdbench

A reproducible benchmark framework for evaluating long-range dependence estimators on canonical, contaminated, and observational time series.

Documentation: lrdbench.readthedocs.io (built with MkDocs and Read the Docs).

Current public release: 1.2.1. Releases are archived on Zenodo — cite the concept DOI 10.5281/zenodo.20937726 (it always resolves to the latest archived version) or use CITATION.cff.

lrdbench is a research-oriented benchmarking framework for studying the behaviour of long-range dependence (LRD) estimators across three distinct settings:

• ground-truth mode for canonical synthetic time series with declared target truth;
• stress-test mode for synthetic time series under controlled contamination;
• observational mode for biomedical or user-provided time series without benchmark truth.

The framework is designed to support:

• rigorous comparison of classical and new LRD estimators;
• bundled temporal, spectral, geometric, wavelet, aggregation, and data-driven estimator families;
• uncertainty-aware benchmarking, including empirical interval coverage where applicable;
• robustness analysis under the bundled contamination operators: heavy-tailed noise, level shifts, outliers, and polynomial trends;
• experimental data-driven baselines, including Random Forest, SVR, CNN, and LSTM estimators;
• transparent failure analysis and validity-rate reporting;
• manifest-driven, provenance-complete, reproducible benchmark execution.

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Why this project exists

There is currently no widely adopted, comprehensive, reproducible benchmark specifically designed for long-range dependence estimation that simultaneously addresses:

• canonical synthetic processes with known targets;
• contamination-induced estimator instability;
• uncertainty quantification and interval coverage;
• observational biomedical time series with no benchmark truth;
• extensible enrolment of new estimators under a common interface.

lrdbench aims to fill that gap.

It is especially intended to support the careful evaluation of the hypothesis that many classical second-order LRD estimators behave well in their intended stationary finite-variance regime, but become unstable, miscalibrated, or non-identifiable under nonstationarity, heavy-tailed fluctuations, artefacts, and other out-of-regime conditions.

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Scope

lrdbench is a research benchmark framework. It is not:

• a clinical decision system;
• a diagnostic tool;
• a guarantee of “true LRD” in arbitrary empirical signals;
• a universal ranking oracle for all estimators in all regimes.

Benchmark results must always be interpreted in light of:

• the declared benchmark mode;
• the target estimand;
• the source specification;
• the contamination design;
• the metric definitions;
• the aggregation and leaderboard rules.

See RESEARCH_USAGE.md for the full policy.

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Core features

Benchmark modes

• Ground-truth mode

  • bias, MAE, RMSE
  • empirical coverage
  • interval width
  • validity rate
  • runtime and efficiency

• Stress-test mode

  • estimate drift
  • degradation ratios
  • validity collapse
  • coverage collapse
  • robustness leaderboards

• Observational mode

  • instability across windows
  • preprocessing sensitivity
  • resampling variability
  • failure analysis
  • stability leaderboards

Supported data sources

• implemented synthetic generators:
  • fGn
  • fBm
  • ARFIMA(0,d,0)
  • MRW
  • fOU
• contaminated synthetic pipelines
• custom CSV datasets
• future observational/API-based datasets

Reporting

• HTML reports
• Markdown reports
• CSV exports
• Parquet result stores
• JSON metadata exports
• LaTeX tables for publication workflows

Extensibility

• pluggable estimator interface
• manifest-driven benchmark runs
• explicit estimator metadata and estimand declarations
• run-local supervised training for built-in ML/NN baseline estimators
• registry-based component enrolment

Bundled estimators

• temporal Hurst-proxy methods: RS, DFA, DMA, AbsoluteMoment, Variance, VarianceResidual
• spectral long-memory methods: GPH, Periodogram, WhittleMLE, ModifiedLocalWhittle
• geometric and wavelet Hurst-proxy comparators
• experimental data-driven baselines: MLRandomForest, MLSVR, MLCNN, MLLSTM

See docs/bundled_estimators.md and docs/estimator_status.md for names, targets, and interpretation status.

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Design principles

lrdbench is built around a few non-negotiable principles:

1. Explicit estimands
   Every estimator must declare the quantity it is intended to estimate.

2. Mode-aware evaluation
   Truth-based metrics are not used where truth does not exist.

3. Failure transparency
   Invalid outputs, crashes, and missing uncertainty are recorded explicitly.

4. Provenance preservation
   Every benchmark result is traceable to a manifest, source, estimator configuration, and software version.

5. Reproducibility first
   A benchmark run should be reproducible from a single manifest plus the relevant package version and data sources.

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Installation

Core installation

pip install lrdbench

First run

After installing with reporting support, run the smallest packaged benchmark:

pip install "lrdbench[reports]"
lrdbench run smoke_ground_truth

From a repository checkout, the same quickstart is available as:

pip install -e ".[reports]"
python examples/quickstart_pure.py

The command prints the run identifier, result store, HTML report path, and output validation command. See the quickstart tutorial for the full walkthrough.

Preview before running

Use --dry-run when you want to inspect the benchmark grid before fitting estimators or writing outputs:

lrdbench run smoke_ground_truth --dry-run

The preview prints the benchmark mode, materialised record count, enrolled estimator count, total fit jobs, clean/contaminated split for stress tests, and global seed. This is the recommended first step before launching public-medium, custom, or data-driven suites.

Data-driven smoke benchmark

The RF/SVR data-driven smoke suite uses optional scikit-learn dependencies:

pip install "lrdbench[ml,reports]"
lrdbench run smoke_data_driven

From a repository checkout:

pip install -e ".[ml,reports]"
lrdbench run configs/suites/smoke_data_driven.yaml
python examples/data_driven_baseline_benchmark.py

Data-driven estimators are trained from the manifest-declared ml_training block and should be interpreted relative to that synthetic training distribution. See docs/data_driven_estimators.md.