{"id":29679600,"url":"https://github.com/dr-saad-la/timeseries-hub","last_synced_at":"2025-07-23T01:34:58.353Z","repository":{"id":305178339,"uuid":"923250834","full_name":"dr-saad-la/timeseries-hub","owner":"dr-saad-la","description":"🚀 Comprehensive collection of time series forecasting models: from classical ARIMA to state-of-the-art deep learning. 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Ready-to-use implementations with benchmarks and tutorials.\n\n## Features\n\n- **Classical Methods**: ARIMA, exponential smoothing, seasonal decomposition\n- **Machine Learning**: Linear models, tree-based methods, ensemble techniques\n- **Deep Learning**: RNN/LSTM, CNN, Transformers, attention models\n- **Benchmarks**: Performance comparisons across methods and datasets\n- **Tutorials**: Step-by-step guides and real-world case studies\n- **Production Ready**: Clean APIs with comprehensive testing\n\n## Quick Start\n\n```bash\n# Clone the repository\ngit clone https://github.com/dr-saad-la/timeseries-hub.git\ncd timeseries-hub\n\n# Install dependencies\npip install -r requirements.txt\n\n# Run a simple forecast\npython -c \"from classical.arima import ARIMAModel; model = ARIMAModel(); print('Ready to forecast!')\"\n```\n\n## Repository Structure\n\n```\ntimeseries-hub/\n├── README.md\n├── LICENSE\n├── CONTRIBUTING.md\n├── requirements.txt\n├── environment.yml\n├── setup.py\n│\n├── docs/\n│   ├── getting-started.md\n│   ├── model-comparison.md\n│   └── api-reference.md\n│\n├── classical/\n│   ├── README.md\n│   ├── arima/\n│   ├── exponential-smoothing/\n│   ├── seasonal-decomposition/\n│   └── statistical-tests/\n│\n├── machine-learning/\n│   ├── README.md\n│   ├── linear-models/\n│   ├── tree-based/\n│   ├── ensemble/\n│   └── feature-engineering/\n│\n├── deep-learning/\n│   ├── README.md\n│   ├── rnn-lstm/\n│   ├── cnn/\n│   ├── transformers/\n│   ├── attention-models/\n│   └── foundation-models/\n│\n├── datasets/\n│   ├── README.md\n│   ├── synthetic/\n│   ├── real-world/\n│   └── benchmarks/\n│\n├── benchmarks/\n│   ├── evaluation-metrics/\n│   ├── model-comparison/\n│   └── performance-reports/\n│\n├── notebooks/\n│   ├── tutorials/\n│   ├── case-studies/\n│   └── experiments/\n│\n├── utils/\n│   ├── data-preprocessing/\n│   ├── visualization/\n│   └── evaluation/\n│\n└── tests/\n    ├── unit/\n    └── integration/\n```\n\n## Installation\n\n### Using pip\n```bash\npip install -r requirements.txt\n```\n\n### Using conda\n```bash\nconda env create -f environment.yml\nconda activate timeseries-hub\n```\n\n### Development Setup\n```bash\npip install -e .\npre-commit install\n```\n\n## Usage Examples\n\n### Classical Models\n```python\nfrom classical.arima import ARIMAModel\nfrom datasets.synthetic import generate_ts_data\n\n# Generate sample data\ndata = generate_ts_data(n_points=1000)\n\n# Fit ARIMA model\nmodel = ARIMAModel(order=(1, 1, 1))\nmodel.fit(data)\nforecast = model.predict(steps=30)\n```\n\n### Machine Learning\n```python\nfrom machine_learning.ensemble import RandomForestForecaster\nfrom utils.feature_engineering import create_features\n\n# Create features and train model\nX, y = create_features(data, window_size=10)\nmodel = RandomForestForecaster()\nmodel.fit(X, y)\npredictions = model.predict(X[-10:])\n```\n\n### Deep Learning\n```python\nfrom deep_learning.transformers import TimeSeriesTransformer\nfrom utils.data_preprocessing import prepare_sequences\n\n# Prepare data and train transformer\nX, y = prepare_sequences(data, seq_length=50)\nmodel = TimeSeriesTransformer(d_model=128, nhead=8)\nmodel.fit(X, y, epochs=100)\nforecast = model.predict(X[-1:], steps=30)\n```\n\n## Model Performance\n\n| Model Type | MAE | RMSE | MAPE | Training Time |\n|------------|-----|------|------|---------------|\n| ARIMA | 0.045 | 0.062 | 4.2% | 2.3s |\n| XGBoost | 0.038 | 0.051 | 3.8% | 12.1s |\n| LSTM | 0.031 | 0.043 | 3.1% | 145.7s |\n| Transformer | 0.027 | 0.039 | 2.8% | 298.4s |\n\n*Results on M4 Competition dataset (average across series)*\n\n## Documentation\n\n- [Getting Started Guide](docs/getting-started.md)\n- [Model Comparison](docs/model-comparison.md)\n- [API Reference](docs/api-reference.md)\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Guidelines\n- Follow PEP 8 style guidelines\n- Write comprehensive tests for new features\n- Update documentation for API changes\n- Run `black` and `flake8` before submitting\n\n## Citation\n\nIf you use this repository in your research, please cite:\n\n```bibtex\n@software{timeseries_hub,\n  title = {timeseries-hub: Comprehensive Time Series Forecasting Models},\n  author = {Your Name},\n  url = {https://github.com/yourusername/timeseries-hub},\n  year = {2025}\n}\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- Contributors and maintainers\n- Open source time series libraries\n- Research papers and datasets used for benchmarking\n\n---\n\n⭐ **Star this repository** if you find it helpful!\n\n📧 **Questions?** Open an issue or start a discussion.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdr-saad-la%2Ftimeseries-hub","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdr-saad-la%2Ftimeseries-hub","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdr-saad-la%2Ftimeseries-hub/lists"}