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
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.
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