# LRDBenchmark
A comprehensive, reproducible framework for Long-Range Dependence (LRD) estimation and benchmarking across Classical, Machine Learning, and Neural Network methods.
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
## ๐ Features
**Comprehensive Estimator Suite:**
- **8+ Classical Methods**: R/S, DFA, DMA, Higuchi, Periodogram, GPH, Whittle, CWT, and more
- **3 Machine Learning Models**: Random Forest, SVR, Gradient Boosting with optimized hyperparameters
- **4 Neural Network Architectures**: LSTM, GRU, CNN, Transformer with pre-trained models
- **Generalized Hurst Exponent (GHE)**: Advanced multifractal analysis capabilities
**Robust Heavy-Tail Analysis:**
- ฮฑ-stable distribution modeling for heavy-tailed time series
- Adaptive preprocessing: standardization, winsorization, log-winsorization, detrending
- Contamination-aware estimation with intelligent fallback mechanisms
**High-Performance Computing:**
- Intelligent optimization backend with graceful fallbacks: JAX โ Numba โ NumPy
- GPU acceleration support where available
- Optimized implementations for large-scale analysis
**Comprehensive Benchmarking:**
- End-to-end benchmarking scripts with statistical analysis
- Confidence intervals, significance tests, and effect size calculations
- Performance leaderboards and comparative analysis tools
**๐ Demonstration Notebooks:**
- **5 Comprehensive Jupyter Notebooks** showcasing all library features
- **Data Generation & Visualization**: All stochastic models with comprehensive plots
- **Estimation & Validation**: All estimator categories with statistical validation
- **Custom Models & Estimators**: Library extensibility and custom implementations
- **Comprehensive Benchmarking**: Full benchmarking system with contamination testing
- **Leaderboard Generation**: Performance rankings and comparative analysis
## ๐ง Quick Start
### Basic Usage
```python
from lrdbenchmark.analysis.temporal.rs.rs_estimator_unified import RSEstimator
from lrdbenchmark.models.data_models.fbm.fbm_model import FractionalBrownianMotion
# Generate synthetic fractional Brownian motion
fbm = FractionalBrownianMotion(H=0.7, sigma=1.0)
x = fbm.generate(n=1000, seed=42)
# Estimate Hurst parameter using R/S analysis
estimator = RSEstimator()
result = estimator.estimate(x)
print(f"Estimated H: {result['hurst_parameter']:.3f}") # ~0.7
```
### Advanced Benchmarking
```python
from lrdbenchmark.analysis.benchmark import ComprehensiveBenchmark
# Run comprehensive benchmark across multiple estimators
benchmark = ComprehensiveBenchmark()
results = benchmark.run_classical_estimators(
data_models=['fbm', 'fgn', 'arfima'],
n_samples=1000,
n_trials=100
)
benchmark.generate_leaderboard(results)
```
### Heavy-Tail Robustness Analysis
```python
from lrdbenchmark.models.data_models.alpha_stable.alpha_stable_model import AlphaStableModel
from lrdbenchmark.robustness.adaptive_preprocessor import AdaptivePreprocessor
# Generate heavy-tailed ฮฑ-stable process
alpha_stable = AlphaStableModel(alpha=1.5, beta=0.0, scale=1.0)
x = alpha_stable.generate(n=1000, seed=42)
# Apply adaptive preprocessing for robust estimation
preprocessor = AdaptivePreprocessor()
x_processed = preprocessor.preprocess(x, method='auto')
# Estimate with robust preprocessing
estimator = RSEstimator()
result = estimator.estimate(x_processed)
```
## ๐ฆ Installation
### From PyPI (Recommended)
```bash
pip install lrdbenchmark
```
### Development Installation
```bash
git clone https://github.com/dave2k77/LRDBenchmark.git
cd LRDBenchmark
pip install -e .
```
### Optional Dependencies
For enhanced performance and additional features:
```bash
# GPU acceleration (JAX)
pip install "lrdbenchmark[jax]"
# Documentation building
pip install "lrdbenchmark[docs]"
# Development tools
pip install "lrdbenchmark[dev]"
```
## ๐ Documentation
- **๐ Full Documentation**: [https://lrdbenchmark.readthedocs.io/](https://lrdbenchmark.readthedocs.io/)
- **๐ Quick Start Guide**: [`docs/quickstart.rst`](docs/quickstart.rst)
- **๐ก Examples**: [`docs/examples/`](docs/examples/) and [`examples/`](examples/)
- **๐ง API Reference**: [API Documentation](https://lrdbenchmark.readthedocs.io/en/latest/api/)
- **๐ Demonstration Notebooks**: [`notebooks/`](notebooks/) - 5 comprehensive Jupyter notebooks showcasing all features
## ๐๏ธ Project Structure
```
LRDBenchmark/
โโโ lrdbenchmark/ # Main package
โ โโโ analysis/ # Estimator implementations
โ โโโ models/ # Data generation models
โ โโโ analytics/ # Performance monitoring
โ โโโ robustness/ # Heavy-tail robustness tools
โโโ notebooks/ # Demonstration notebooks (5 comprehensive Jupyter notebooks)
โโโ scripts/ # Benchmarking and analysis scripts
โโโ examples/ # Usage examples
โโโ docs/ # Documentation
โโโ tests/ # Test suite
โโโ tools/ # Development utilities
โโโ config/ # Configuration files
```
## ๐ ๏ธ Available Estimators
### Classical Methods
- **R/S Analysis** - Rescaled Range analysis
- **DFA** - Detrended Fluctuation Analysis
- **DMA** - Detrended Moving Average
- **Higuchi** - Higuchi's fractal dimension method
- **Periodogram** - Periodogram-based estimation
- **GPH** - Geweke and Porter-Hudak estimator
- **Whittle** - Whittle maximum likelihood
- **CWT** - Continuous Wavelet Transform
- **GHE** - Generalized Hurst Exponent
### Machine Learning
- **Random Forest** - Ensemble tree-based estimation
- **Support Vector Regression** - SVM-based estimation
- **Gradient Boosting** - Boosted tree estimation
### Neural Networks
- **LSTM** - Long Short-Term Memory networks
- **GRU** - Gated Recurrent Units
- **CNN** - Convolutional Neural Networks
- **Transformer** - Attention-based architectures
## ๐ Demonstration Notebooks
LRDBenchmark includes 5 comprehensive Jupyter notebooks that demonstrate all library features:
### 1. Data Generation and Visualization
**File**: `notebooks/01_data_generation_and_visualisation.ipynb`
Demonstrates all available data models with comprehensive visualizations:
- **FBM/FGN**: Fractional Brownian Motion and Gaussian Noise
- **ARFIMA**: Autoregressive Fractionally Integrated Moving Average
- **MRW**: Multifractal Random Walk
- **Alpha-Stable**: Heavy-tailed distributions
- **Visualizations**: Time series, ACF, PSD, distributions
- **Quality Assessment**: Statistical validation and theoretical properties
### 2. Estimation and Statistical Validation
**File**: `notebooks/02_estimation_and_validation.ipynb`
Covers all estimator categories with statistical validation:
- **Classical**: R/S, DFA, DMA, Higuchi, GPH, Whittle, Periodogram, CWT
- **Machine Learning**: Random Forest, SVR, Gradient Boosting
- **Neural Networks**: CNN, LSTM, GRU, Transformer
- **Statistical Validation**: Confidence intervals, bootstrap methods
- **Performance Comparison**: Accuracy, speed, and reliability analysis
### 3. Custom Models and Estimators
**File**: `notebooks/03_custom_models_and_estimators.ipynb`
Shows how to extend the library with custom components:
- **Custom Data Models**: Fractional Ornstein-Uhlenbeck process
- **Custom Estimators**: Variance-Based Hurst Estimator
- **Library Extensibility**: Base classes and integration patterns
- **Best Practices**: Guidelines for custom implementations
### 4. Comprehensive Benchmarking
**File**: `notebooks/04_comprehensive_benchmarking.ipynb`
Demonstrates the full benchmarking system:
- **Benchmark Types**: Classical, ML, Neural, Comprehensive
- **Contamination Testing**: Noise, outliers, trends, seasonal patterns
- **Performance Metrics**: MAE, execution time, success rate
- **Statistical Analysis**: Confidence intervals and significance tests
### 5. Leaderboard Generation
**File**: `notebooks/05_leaderboard_generation.ipynb`
Shows performance ranking and comparative analysis:
- **Performance Rankings**: Overall and category-wise leaderboards
- **Composite Scoring**: Accuracy, speed, and robustness metrics
- **Visualization**: Performance plots and comparison tables
- **Export Options**: CSV, JSON, LaTeX formats
### Getting Started with Notebooks
```bash
# Clone the repository
git clone https://github.com/dave2k77/LRDBenchmark.git
cd LRDBenchmark
# Install dependencies
pip install -e .
pip install jupyter matplotlib seaborn
# Start Jupyter
jupyter notebook notebooks/
```
Each notebook is self-contained, well-documented, and provides a complete learning path from basic concepts to advanced applications.
## ๐งช Testing
Run the test suite:
```bash
# Basic tests
python -m pytest tests/
# With coverage
python -m pytest tests/ --cov=lrdbenchmark --cov-report=html
```
## ๐ค Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## ๐ License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## ๐ Acknowledgments
- Built with modern Python scientific computing stack
- Leverages JAX for high-performance computing
- Inspired by the need for reproducible LRD analysis
- Community-driven development and validation
## ๐ Support
- **Issues**: [GitHub Issues](https://github.com/dave2k77/LRDBenchmark/issues)
- **Discussions**: [GitHub Discussions](https://github.com/dave2k77/LRDBenchmark/discussions)
- **Documentation**: [ReadTheDocs](https://lrdbenchmark.readthedocs.io/)
---
**Made with โค๏ธ for the time series analysis community**
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"description": "# LRDBenchmark\n\nA comprehensive, reproducible framework for Long-Range Dependence (LRD) estimation and benchmarking across Classical, Machine Learning, and Neural Network methods.\n\n[](https://opensource.org/licenses/MIT)\n[](https://www.python.org/downloads/)\n\n## \ud83d\ude80 Features\n\n**Comprehensive Estimator Suite:**\n- **8+ Classical Methods**: R/S, DFA, DMA, Higuchi, Periodogram, GPH, Whittle, CWT, and more\n- **3 Machine Learning Models**: Random Forest, SVR, Gradient Boosting with optimized hyperparameters \n- **4 Neural Network Architectures**: LSTM, GRU, CNN, Transformer with pre-trained models\n- **Generalized Hurst Exponent (GHE)**: Advanced multifractal analysis capabilities\n\n**Robust Heavy-Tail Analysis:**\n- \u03b1-stable distribution modeling for heavy-tailed time series\n- Adaptive preprocessing: standardization, winsorization, log-winsorization, detrending\n- Contamination-aware estimation with intelligent fallback mechanisms\n\n**High-Performance Computing:**\n- Intelligent optimization backend with graceful fallbacks: JAX \u2192 Numba \u2192 NumPy\n- GPU acceleration support where available\n- Optimized implementations for large-scale analysis\n\n**Comprehensive Benchmarking:**\n- End-to-end benchmarking scripts with statistical analysis\n- Confidence intervals, significance tests, and effect size calculations\n- Performance leaderboards and comparative analysis tools\n\n**\ud83d\udcda Demonstration Notebooks:**\n- **5 Comprehensive Jupyter Notebooks** showcasing all library features\n- **Data Generation & Visualization**: All stochastic models with comprehensive plots\n- **Estimation & Validation**: All estimator categories with statistical validation\n- **Custom Models & Estimators**: Library extensibility and custom implementations\n- **Comprehensive Benchmarking**: Full benchmarking system with contamination testing\n- **Leaderboard Generation**: Performance rankings and comparative analysis\n\n## \ud83d\udd27 Quick Start\n\n### Basic Usage\n\n```python\nfrom lrdbenchmark.analysis.temporal.rs.rs_estimator_unified import RSEstimator\nfrom lrdbenchmark.models.data_models.fbm.fbm_model import FractionalBrownianMotion\n\n# Generate synthetic fractional Brownian motion\nfbm = FractionalBrownianMotion(H=0.7, sigma=1.0)\nx = fbm.generate(n=1000, seed=42)\n\n# Estimate Hurst parameter using R/S analysis\nestimator = RSEstimator()\nresult = estimator.estimate(x)\nprint(f\"Estimated H: {result['hurst_parameter']:.3f}\") # ~0.7\n```\n\n### Advanced Benchmarking\n\n```python\nfrom lrdbenchmark.analysis.benchmark import ComprehensiveBenchmark\n\n# Run comprehensive benchmark across multiple estimators\nbenchmark = ComprehensiveBenchmark()\nresults = benchmark.run_classical_estimators(\n data_models=['fbm', 'fgn', 'arfima'],\n n_samples=1000,\n n_trials=100\n)\nbenchmark.generate_leaderboard(results)\n```\n\n### Heavy-Tail Robustness Analysis\n\n```python\nfrom lrdbenchmark.models.data_models.alpha_stable.alpha_stable_model import AlphaStableModel\nfrom lrdbenchmark.robustness.adaptive_preprocessor import AdaptivePreprocessor\n\n# Generate heavy-tailed \u03b1-stable process\nalpha_stable = AlphaStableModel(alpha=1.5, beta=0.0, scale=1.0)\nx = alpha_stable.generate(n=1000, seed=42)\n\n# Apply adaptive preprocessing for robust estimation\npreprocessor = AdaptivePreprocessor()\nx_processed = preprocessor.preprocess(x, method='auto')\n\n# Estimate with robust preprocessing\nestimator = RSEstimator()\nresult = estimator.estimate(x_processed)\n```\n\n## \ud83d\udce6 Installation\n\n### From PyPI (Recommended)\n\n```bash\npip install lrdbenchmark\n```\n\n### Development Installation\n\n```bash\ngit clone https://github.com/dave2k77/LRDBenchmark.git\ncd LRDBenchmark\npip install -e .\n```\n\n### Optional Dependencies\n\nFor enhanced performance and additional features:\n\n```bash\n# GPU acceleration (JAX)\npip install \"lrdbenchmark[jax]\"\n\n# Documentation building\npip install \"lrdbenchmark[docs]\"\n\n# Development tools\npip install \"lrdbenchmark[dev]\"\n```\n\n## \ud83d\udcda Documentation\n\n- **\ud83d\udcd6 Full Documentation**: [https://lrdbenchmark.readthedocs.io/](https://lrdbenchmark.readthedocs.io/)\n- **\ud83d\ude80 Quick Start Guide**: [`docs/quickstart.rst`](docs/quickstart.rst)\n- **\ud83d\udca1 Examples**: [`docs/examples/`](docs/examples/) and [`examples/`](examples/)\n- **\ud83d\udd27 API Reference**: [API Documentation](https://lrdbenchmark.readthedocs.io/en/latest/api/)\n- **\ud83d\udcd3 Demonstration Notebooks**: [`notebooks/`](notebooks/) - 5 comprehensive Jupyter notebooks showcasing all features\n\n## \ud83c\udfd7\ufe0f Project Structure\n\n```\nLRDBenchmark/\n\u251c\u2500\u2500 lrdbenchmark/ # Main package\n\u2502 \u251c\u2500\u2500 analysis/ # Estimator implementations\n\u2502 \u251c\u2500\u2500 models/ # Data generation models\n\u2502 \u251c\u2500\u2500 analytics/ # Performance monitoring\n\u2502 \u2514\u2500\u2500 robustness/ # Heavy-tail robustness tools\n\u251c\u2500\u2500 notebooks/ # Demonstration notebooks (5 comprehensive Jupyter notebooks)\n\u251c\u2500\u2500 scripts/ # Benchmarking and analysis scripts\n\u251c\u2500\u2500 examples/ # Usage examples\n\u251c\u2500\u2500 docs/ # Documentation\n\u251c\u2500\u2500 tests/ # Test suite\n\u251c\u2500\u2500 tools/ # Development utilities\n\u2514\u2500\u2500 config/ # Configuration files\n```\n\n## \ud83d\udee0\ufe0f Available Estimators\n\n### Classical Methods\n- **R/S Analysis** - Rescaled Range analysis\n- **DFA** - Detrended Fluctuation Analysis \n- **DMA** - Detrended Moving Average\n- **Higuchi** - Higuchi's fractal dimension method\n- **Periodogram** - Periodogram-based estimation\n- **GPH** - Geweke and Porter-Hudak estimator\n- **Whittle** - Whittle maximum likelihood\n- **CWT** - Continuous Wavelet Transform\n- **GHE** - Generalized Hurst Exponent\n\n### Machine Learning\n- **Random Forest** - Ensemble tree-based estimation\n- **Support Vector Regression** - SVM-based estimation\n- **Gradient Boosting** - Boosted tree estimation\n\n### Neural Networks\n- **LSTM** - Long Short-Term Memory networks\n- **GRU** - Gated Recurrent Units\n- **CNN** - Convolutional Neural Networks\n- **Transformer** - Attention-based architectures\n\n## \ud83d\udcd3 Demonstration Notebooks\n\nLRDBenchmark includes 5 comprehensive Jupyter notebooks that demonstrate all library features:\n\n### 1. Data Generation and Visualization\n**File**: `notebooks/01_data_generation_and_visualisation.ipynb`\n\nDemonstrates all available data models with comprehensive visualizations:\n- **FBM/FGN**: Fractional Brownian Motion and Gaussian Noise\n- **ARFIMA**: Autoregressive Fractionally Integrated Moving Average\n- **MRW**: Multifractal Random Walk\n- **Alpha-Stable**: Heavy-tailed distributions\n- **Visualizations**: Time series, ACF, PSD, distributions\n- **Quality Assessment**: Statistical validation and theoretical properties\n\n### 2. Estimation and Statistical Validation\n**File**: `notebooks/02_estimation_and_validation.ipynb`\n\nCovers all estimator categories with statistical validation:\n- **Classical**: R/S, DFA, DMA, Higuchi, GPH, Whittle, Periodogram, CWT\n- **Machine Learning**: Random Forest, SVR, Gradient Boosting\n- **Neural Networks**: CNN, LSTM, GRU, Transformer\n- **Statistical Validation**: Confidence intervals, bootstrap methods\n- **Performance Comparison**: Accuracy, speed, and reliability analysis\n\n### 3. Custom Models and Estimators\n**File**: `notebooks/03_custom_models_and_estimators.ipynb`\n\nShows how to extend the library with custom components:\n- **Custom Data Models**: Fractional Ornstein-Uhlenbeck process\n- **Custom Estimators**: Variance-Based Hurst Estimator\n- **Library Extensibility**: Base classes and integration patterns\n- **Best Practices**: Guidelines for custom implementations\n\n### 4. Comprehensive Benchmarking\n**File**: `notebooks/04_comprehensive_benchmarking.ipynb`\n\nDemonstrates the full benchmarking system:\n- **Benchmark Types**: Classical, ML, Neural, Comprehensive\n- **Contamination Testing**: Noise, outliers, trends, seasonal patterns\n- **Performance Metrics**: MAE, execution time, success rate\n- **Statistical Analysis**: Confidence intervals and significance tests\n\n### 5. Leaderboard Generation\n**File**: `notebooks/05_leaderboard_generation.ipynb`\n\nShows performance ranking and comparative analysis:\n- **Performance Rankings**: Overall and category-wise leaderboards\n- **Composite Scoring**: Accuracy, speed, and robustness metrics\n- **Visualization**: Performance plots and comparison tables\n- **Export Options**: CSV, JSON, LaTeX formats\n\n### Getting Started with Notebooks\n\n```bash\n# Clone the repository\ngit clone https://github.com/dave2k77/LRDBenchmark.git\ncd LRDBenchmark\n\n# Install dependencies\npip install -e .\npip install jupyter matplotlib seaborn\n\n# Start Jupyter\njupyter notebook notebooks/\n```\n\nEach notebook is self-contained, well-documented, and provides a complete learning path from basic concepts to advanced applications.\n\n## \ud83e\uddea Testing\n\nRun the test suite:\n\n```bash\n# Basic tests\npython -m pytest tests/\n\n# With coverage\npython -m pytest tests/ --cov=lrdbenchmark --cov-report=html\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\ude4f Acknowledgments\n\n- Built with modern Python scientific computing stack\n- Leverages JAX for high-performance computing\n- Inspired by the need for reproducible LRD analysis\n- Community-driven development and validation\n\n## \ud83d\udcde Support\n\n- **Issues**: [GitHub Issues](https://github.com/dave2k77/LRDBenchmark/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/dave2k77/LRDBenchmark/discussions)\n- **Documentation**: [ReadTheDocs](https://lrdbenchmark.readthedocs.io/)\n\n---\n\n**Made with \u2764\ufe0f for the time series analysis community**\n\n\n\n\n\n\n\n\n\n",
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