https://github.com/kyegomez/chronoformer
A production-grade implementation of a memory-efficient transformer specifically designed for tabular time series data.
https://github.com/kyegomez/chronoformer
ai finance finance-ai ml pytorch tensorflow time-series transformer transformers
Last synced: 10 months ago
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A production-grade implementation of a memory-efficient transformer specifically designed for tabular time series data.
- Host: GitHub
- URL: https://github.com/kyegomez/chronoformer
- Owner: kyegomez
- License: mit
- Created: 2024-12-12T05:46:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-13T19:54:14.000Z (over 1 year ago)
- Last Synced: 2025-08-16T02:42:34.654Z (11 months ago)
- Topics: ai, finance, finance-ai, ml, pytorch, tensorflow, time-series, transformer, transformers
- Language: Python
- Homepage: https://agoralab.xyz
- Size: 37.1 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# Chronoformer
Memory-Efficient Transformer for Time Series Analysis
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)
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[](https://github.com/kyegomez/swarms)
[](https://www.python.org/downloads/)
[](https://pytorch.org/)
[](https://opensource.org/licenses/MIT)
[](https://github.com/psf/black)
A production-grade implementation of a memory-efficient transformer specifically designed for tabular time series data. This model introduces several optimizations for handling large-scale time series data while maintaining high performance and minimal memory footprint.
## š Key Features
- **Memory Optimization**
- Linear sparse attention mechanism
- Sliding window attention patterns
- Gradient checkpointing
- Efficient memory management with gated units
- **Data Processing**
- Support for CSV, Excel, Parquet, and JSON formats
- Automatic time series parsing and validation
- Memory-efficient data loading for large datasets
- Comprehensive preprocessing pipeline
- **Production Ready**
- Comprehensive logging with loguru
- Type hints and documentation
- Error handling and input validation
- PyTorch Lightning integration
- Scalable architecture
## š Requirements
```text
python>=3.8
torch>=2.0
pandas>=1.3
numpy>=1.20
pyarrow>=7.0
scikit-learn>=1.0
loguru>=0.6
```
## š§ Installation
```bash
# Clone the repository
git clone https://github.com/kyegomez/ChronoFormer.git
cd efficient-time-series-transformer
# Install dependencies
pip install -r requirements.txt
```
## šÆ Quick Start
```python
from transformer import EfficientTransformer, TransformerConfig
from preprocessing import TimeSeriesPreprocessor
# Initialize preprocessor
preprocessor = TimeSeriesPreprocessor(
time_column="timestamp",
feature_columns=["temperature", "humidity", "pressure"],
sequence_length=24,
batch_size=32
)
# Load and preprocess data
sequences = preprocessor.preprocess("your_data.csv")
dataloader = preprocessor.create_dataloader(sequences)
# Initialize model
config = TransformerConfig(
num_features=3,
max_seq_length=1000,
d_model=256
)
model = EfficientTransformer(config)
# Training
for batch in dataloader:
features, timestamps, targets = batch
predictions = model(features, timestamps)
# Your training logic here
```
## šļø Architecture
The model consists of several key components:
1. **Linear Sparse Attention**
- Reduces memory complexity from O(nĀ²) to O(n)
- Implements sliding window attention patterns
- Maintains performance while reducing memory usage
2. **Gated Memory Unit**
- Controls information flow
- Manages memory states efficiently
- Reduces redundant information storage
3. **Temporal Compression**
- Reduces sequence length adaptively
- Preserves important temporal patterns
- Optimizes memory usage for long sequences
4. **Data Pipeline**
- Efficient data loading and preprocessing
- Automatic feature scaling and normalization
- Time-based feature engineering
## š Performance
| Dataset Size | Sequence Length | Memory Usage | Training Time/Epoch | MAE | RMSE |
|--------------|----------------|--------------|-------------------|------|------|
| Small (<10K) | 100 | 0.5GB | 2min | 0.15 | 0.22 |
| Medium (<100K)| 500 | 2.1GB | 15min | 0.18 | 0.25 |
| Large (<1M) | 1000 | 4.8GB | 45min | 0.21 | 0.28 |
## š Usage Examples
### Basic Usage
```python
from transformer import create_transformer
# Create model with default settings
model = create_transformer(
num_features=10,
max_seq_length=1000
)
# Generate predictions
predictions = model.predict(x, timestamps)
```
### Advanced Configuration
```python
config = TransformerConfig(
d_model=512,
n_heads=8,
n_layers=6,
dropout=0.1,
max_seq_length=2000,
feature_dim=128,
compression_factor=4,
attention_window=100
)
model = EfficientTransformer(config)
```
### Custom Data Loading
```python
preprocessor = TimeSeriesPreprocessor(
time_column="timestamp",
feature_columns=["feature1", "feature2"],
target_columns=["target"],
sequence_length=100,
stride=1,
scaling_method='standard',
fill_method='forward'
)
# Load and preprocess data
sequences = preprocessor.preprocess(
"data.parquet",
chunk_size=10000
)
```
## š¤ Contributing
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## š License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## š® Contact
- Your Name - [@kyegomez](https://twitter.com/kyegomezb)
- Project Link: [https://github.com/yourusername/efficient-time-series-transformer](https://github.com/kyegomez/ChronoFormer)
## š Acknowledgments
- The PyTorch team for their excellent framework
- All contributors who have helped to improve this project
## š Citation
If you use this model in your research, please cite:
```bibtex
@software{chronoformer,
author = {Kye Gomez},
title = {Memory-Efficient Transformer for Time Series Analysis},
year = {2024},
publisher = {GitHub},
url = {https://github.com/kyegomez/ChronoFormer}
}
```