The source code of the paper : "Retail time series forecasting using an automated deep meta-learning framework"

Use the package manager pip to install dependencies:

pip install -r requirements.txt

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The code used wandb for the hyper-parameter optimization. You can connect to your account by:

wandb login

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You can run the optimization by:

python src/experiment.py

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The directories fforma and M0 contain the source code of the benchmark models.

The base-forecasters' code, in R, can be found from this repository, and forecasts for 1, 4, and 7 steps are in the directory base-forecasters.

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The results of the parameters that had the lowest validation error are saved in the directory results_final, and results_final\analysis.ipynb shows the RMSEs, AvgRelRMSEs, and AvgRelMAEs of the paper.

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The IRI dataset is in the directory src\dataset.