Instructions to use mnigr/patchtst-mean-reversion-scanner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mnigr/patchtst-mean-reversion-scanner with Transformers:
# Load model directly from transformers import AutoTokenizer, PatchTSTForClassification tokenizer = AutoTokenizer.from_pretrained("mnigr/patchtst-mean-reversion-scanner") model = PatchTSTForClassification.from_pretrained("mnigr/patchtst-mean-reversion-scanner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - crypto | |
| - trading | |
| - patchtst | |
| - transformers | |
| - time-series | |
| # PatchTST Mean Reversion Crypto Scanner | |
| This model uses the **PatchTST** architecture (A Time Series is Worth 64 Words) to identify high-probability mean reversion setups in cryptocurrency perpetual futures. | |
| Unlike XGBoost which looks at single snapshots, this model analyzes a 48-step sequence of market conditions leading up to a signal. | |