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
Ctrl+K
- deep_learning_models
- deployment_package
- dl_multi_horizon_cv_out
- dl_multi_horizon_finalized_improved_out
- dl_multi_horizon_finalized_out
- dl_multi_horizon_out_safe_v2
- dl_multi_horizon_out_safe_v3
- dl_multi_horizon_rewrite_out
- eval_results
- logs
- m5_memory_safe
- models
- models_and_views
- models_dl
- models_safe
- moe_eval
- plots
- pytorch_finalized_fixed
- pytorch_models
- trained_models
- 2.13 kB
- 3.33 kB