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
Add dl_multi_horizon_finalized_out
Browse files- dl_multi_horizon_finalized_out/best_model.pth +3 -0
- dl_multi_horizon_finalized_out/dl_results.json +86 -0
- dl_multi_horizon_finalized_out/feature_cols.joblib +3 -0
- dl_multi_horizon_finalized_out/final_model.pth +3 -0
- dl_multi_horizon_finalized_out/label_encoder.joblib +3 -0
- dl_multi_horizon_finalized_out/preprocess_artifacts.joblib +3 -0
- dl_multi_horizon_finalized_out/scaler.joblib +3 -0
- dl_multi_horizon_finalized_out/training_history.json +1 -0
dl_multi_horizon_finalized_out/best_model.pth
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oid sha256:7979054f473443ccb303fa8a8d647b7f67032be3a85062870ff094d8fdc7f824
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size 196533
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dl_multi_horizon_finalized_out/dl_results.json
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"per_h_test": [
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"per_item_rmse_summary": {
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"count": 300.0,
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}
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dl_multi_horizon_finalized_out/feature_cols.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:3aef3269f630e72c0c27d51bb858e01d73a9fd24a41aa52a220a7a6bc5a58a38
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size 118
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dl_multi_horizon_finalized_out/final_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:79cb7a1a3d3db289ee9a16351d9d4819a2d08502102399190cde9bd7178ab4df
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size 196556
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dl_multi_horizon_finalized_out/label_encoder.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:44b9e05b06a5fe58ca782a3452babb9b7418bad5041bc63f563df511506264ca
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size 4935
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dl_multi_horizon_finalized_out/preprocess_artifacts.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:b02707bf2da534a87c6ee232bd2a7d5cae9e668d6cf05da31726690c8c64fc29
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size 8432
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dl_multi_horizon_finalized_out/scaler.joblib
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version https://git-lfs.github.com/spec/v1
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oid sha256:663dddf979dd3fc3e98df2373759579addd157e3159d4ec490fb580b2baf055c
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size 855
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dl_multi_horizon_finalized_out/training_history.json
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{"train_loss": [0.07436066044383491, 0.036863782140888916, 0.031256847965549554, 0.02839615379787044, 0.026664197068686123, 0.0256495341806617, 0.02503066793059474, 0.02447854761428983, 0.023966510305242824, 0.023775404942104752, 0.023600328784584013, 0.023327679175957544, 0.023121965086914057, 0.022958742383719477, 0.02287574251188545, 0.022671510468526983, 0.022340629172714933, 0.022439265808719674, 0.02232578538962646, 0.022238746461217963, 0.022108811866615386, 0.022128133214270044, 0.02182087990523174, 0.021799906567109145, 0.02169045042061549, 0.021601642429557266, 0.02161454324555022, 0.021419982691079576, 0.021367011012028383, 0.021342702481328257, 0.021216578010642372, 0.021184362582122255, 0.021083268016231376, 0.021026037355872575, 0.020858768524836428, 0.020836764934244533, 0.020752903632202883, 0.020654689751886175, 0.02063579781178311, 0.02066088417878017], "val_loss": [0.02720757541246712, 0.02498084888793528, 0.02548294994048774, 0.024528696527704598, 0.022684002481400966, 0.02439464251510799, 0.022990175941959023, 0.021910420292988418, 0.021398797910660506, 0.02263782829977572, 0.02053146795369685, 0.0217036671936512, 0.02122632320970297, 0.020734996907413004, 0.0216108419932425, 0.020414631441235544, 0.020590191707015038, 0.02129587032832205, 0.020622552558779716, 0.021339749498292804, 0.020614211913198233, 0.02027646000497043, 0.020008352398872376, 0.02063575917854905, 0.0198490955401212, 0.021260518534108996, 0.02017310601659119, 0.01959536294452846, 0.020651709102094174, 0.020574192749336362, 0.020632014609873295, 0.01991673931479454, 0.020261244382709265, 0.02002900280058384, 0.020466612372547387, 0.02065399121493101, 0.020254036551341413, 0.02004771577194333, 0.02104086857289076, 0.01978388517163694]}
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