Time Series Forecasting
Joblib
Keras
PyTorch
generic
demand-forecasting
supply-chain
gru
lstm
lightgbm
xgboost
random-forest
mixture-of-experts
Instructions to use AbdullahImran/Saudi-Supply-Demand-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use AbdullahImran/Saudi-Supply-Demand-Models with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://AbdullahImran/Saudi-Supply-Demand-Models") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b4f4479cb5eba8b172868c99fcd1da0e80ffa7eae58e6a8780f1c9a644f4a79
- Size of remote file:
- 2.68 MB
- SHA256:
- b9d7b378c9314cbce73acd1575fd6ea8abddd9d086626f8da36aa4b5da54061d
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