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:
# !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://AbdullahImran/Saudi-Supply-Demand-Models") - Notebooks
- Google Colab
- Kaggle
Download dl_multi_horizon_cv_out/scaler_fold2.joblib from AbdullahImran/Saudi-Supply-Demand-Models: direct link, hf CLI and curl.
- Browser
- Download file 959 Bytes
-
https://huggingface.co/AbdullahImran/Saudi-Supply-Demand-Models/resolve/main/dl_multi_horizon_cv_out/scaler_fold2.joblib
- Command line
-
hf download hf://AbdullahImran/Saudi-Supply-Demand-Models/dl_multi_horizon_cv_out/scaler_fold2.joblib
-
curl -L -o scaler_fold2.joblib https://huggingface.co/AbdullahImran/Saudi-Supply-Demand-Models/resolve/main/dl_multi_horizon_cv_out/scaler_fold2.joblib
959 Bytes
- Xet hash:
- 0845822f85f777898f1a134286cd5ca70192c34933869d98af961e306a560db2
- Size of remote file:
- 959 Bytes
- SHA256:
- 26fdb3e39efa6aa257e3bb834afcd1f5ac7c1c1c53ad5c133e5d4028fbc4d171
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