Instructions to use StockCast/gru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use StockCast/gru with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://StockCast/gru") - Notebooks
- Google Colab
- Kaggle
Download models/AMCL(PRAN)/forecast_next_30.csv from StockCast/gru: direct link, hf CLI and curl.
- Browser
- Download file 666 Bytes
-
https://huggingface.co/StockCast/gru/resolve/main/models/AMCL(PRAN)/forecast_next_30.csv
- Command line
-
hf download 'hf://StockCast/gru/models/AMCL(PRAN)/forecast_next_30.csv'
-
curl -L -o forecast_next_30.csv 'https://huggingface.co/StockCast/gru/resolve/main/models/AMCL(PRAN)/forecast_next_30.csv'
666 Bytes
| step_ahead,prediction | |
| 1,205.38182520270348 | |
| 2,205.80488795638087 | |
| 3,203.6616741627455 | |
| 4,206.04891565442088 | |
| 5,205.28084237575533 | |
| 6,203.72376655936242 | |
| 7,205.48669198453427 | |
| 8,205.40210996270181 | |
| 9,204.0108060896397 | |
| 10,204.48707393705845 | |
| 11,203.52748267352584 | |
| 12,205.09276737272742 | |
| 13,202.49964235126973 | |
| 14,203.97651195526126 | |
| 15,203.71649050414564 | |
| 16,203.8386569976807 | |
| 17,204.17011608183387 | |
| 18,203.84350770115853 | |
| 19,202.21889516711238 | |
| 20,202.09199669361115 | |
| 21,202.1661819279194 | |
| 22,202.25744638741017 | |
| 23,204.51314222812655 | |
| 24,200.9847473025322 | |
| 25,201.35373997688296 | |
| 26,203.40847083330155 | |
| 27,203.8831952750683 | |
| 28,201.92775119543077 | |
| 29,202.24379318356515 | |
| 30,204.1429283976555 | |