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/ARAMIT/forecast_next_30.csv from StockCast/gru: direct link, hf CLI and curl.
- Browser
- Download file 665 Bytes
-
https://huggingface.co/StockCast/gru/resolve/main/models/ARAMIT/forecast_next_30.csv
- Command line
-
hf download hf://StockCast/gru/models/ARAMIT/forecast_next_30.csv
-
curl -L -o forecast_next_30.csv https://huggingface.co/StockCast/gru/resolve/main/models/ARAMIT/forecast_next_30.csv
665 Bytes
| step_ahead,prediction | |
| 1,184.25585655868053 | |
| 2,189.99404745548964 | |
| 3,200.45929177105427 | |
| 4,184.6158375069499 | |
| 5,195.73805874586105 | |
| 6,193.23788790404797 | |
| 7,183.24459435790777 | |
| 8,181.3525611013174 | |
| 9,195.33471586555243 | |
| 10,187.8254103884101 | |
| 11,186.45575745403767 | |
| 12,191.18472328782082 | |
| 13,182.92436877638102 | |
| 14,181.71561854332685 | |
| 15,181.6421672552824 | |
| 16,184.27552117407322 | |
| 17,186.91901920735836 | |
| 18,192.9637369737029 | |
| 19,181.61988346278667 | |
| 20,188.0292592793703 | |
| 21,179.04440088570118 | |
| 22,178.3207700625062 | |
| 23,189.68515085428953 | |
| 24,189.84874548763037 | |
| 25,178.54349365830421 | |
| 26,193.96050015836954 | |
| 27,175.11271464079618 | |
| 28,193.68319997936487 | |
| 29,188.11637809872627 | |
| 30,185.6961960941553 | |