Instructions to use kyLELEng/patchtst-cross-sectional-return-forecast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kyLELEng/patchtst-cross-sectional-return-forecast with Transformers:
# Load model directly from transformers import AutoTokenizer, PatchTSTForPrediction tokenizer = AutoTokenizer.from_pretrained("kyLELEng/patchtst-cross-sectional-return-forecast") model = PatchTSTForPrediction.from_pretrained("kyLELEng/patchtst-cross-sectional-return-forecast") - Notebooks
- Google Colab
- Kaggle
| { | |
| "validation": { | |
| "loss": 40.24222278594971, | |
| "mae": 3.3909754753112793, | |
| "mse": 15.027800559997559, | |
| "directional_accuracy": 0.5080167271784233, | |
| "flattened_ic": 0.002849485427271254, | |
| "cross_sectional_ic": 0.008907554652154311, | |
| "cross_sectional_rank_ic": 0.008295830343493587 | |
| }, | |
| "test": { | |
| "loss": 38.46169090270996, | |
| "mae": 3.328381299972534, | |
| "mse": 14.407476425170898, | |
| "directional_accuracy": 0.534091938405797, | |
| "flattened_ic": 0.00037866420310066716, | |
| "cross_sectional_ic": 0.00456014569165105, | |
| "cross_sectional_rank_ic": 0.009876399072214697 | |
| }, | |
| "best_validation_loss": 40.24222278594971, | |
| "num_train_windows": 2249, | |
| "num_validation_windows": 482, | |
| "num_test_windows": 483, | |
| "num_return_steps": 3789, | |
| "selected_tickers": [ | |
| "AAPL", | |
| "MSFT", | |
| "AMZN", | |
| "GOOGL", | |
| "NVDA", | |
| "TSLA", | |
| "AMD", | |
| "INTC", | |
| "ADBE", | |
| "ORCL", | |
| "CSCO", | |
| "IBM", | |
| "JPM", | |
| "BAC", | |
| "V", | |
| "MA", | |
| "AXP", | |
| "JNJ", | |
| "PG", | |
| "KO" | |
| ], | |
| "data_source": "siddharthmb/stocks-ohlcv" | |
| } |