https://github.com/ashish1610dhiman/bestbuy
Random Samplers
- Ashish Dhiman
- Anshit Verma
- Yibei Hu
bestbuy
│ README.md
│ data.dvc #DVC file for data
│ bestbuy_env.yml #Conda env file
│
└───notebooks
│ │
│ └───src/ Modules for implementations
│ │ ad_hmm.py #Module for HMM implementation
│ │ ad_stl_prophet.py #Module for Prophet/STL/MSTL implementation
│ │ utils.py #Utility function
│ │ ...
│
└───notebooks
│ │
│ └───ashish/ Test notebooks by Ashish | HMM/STL/Prophet
│ │ │ ...
│ │
│ └───ashish_validation_train/ Notebooks for training final models
│ │ │ b.run_hmm_final1.ipynb #Train and forecast from HMMM
│ │ │ c.run_stl_prophet_new.ipynb #Train and forecast from Prophet/ STL/MSTL
│ │ │ ...
│ │
│ └───yibei/ Test notebooks by Yibei | HMM/STL/Prophet
│ │ HW_final.ipynb #Notebook to train Holt Winters Exp smoothing and Null model
│ │ ...
│
│
└───plots/ Folder for plots
│
└───Results/ Folder for RMSE and other results
As listed in the folder structure above, these are the main codes and their description:
- notebooks/ashish/validation_train/b.run_hmm_final1.ipynb: The code is used to implement and train HMM models. It calls upon the ad_hmm.py module in the src folder.
- notebooks/ashish/validation_train/c.run_stl_prophet_new.ipynb: The code is used to implement and train Prophet/ STL/MSTL models. It calls upon the run_stl_prophet_new.py module in the src folder.
- notebooks/yibei/c.HW_final.ipynb: The code is used to implement and train Holt Winters Exp smoothing and the Null model.
- Statmodels beta realease has been used
- Joblib is used
- some codes are run on kaggle, to prevent personal laptop use