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:
- 97c3918eddfe46778d928efbcb6d5a1e4aa825a2fd0d6fdf8e851d8af20a38d7
- Size of remote file:
- 5.11 kB
- SHA256:
- f4dc3637b22d3ee39585d43c486a9704d666898a0884b60d2a1b82332deca03a
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