Bibliography (21):

1. LightGBM: A Highly Efficient Gradient Boosting Decision Tree

2. Kaggle forecasting competitions: An overlooked learning opportunity

3. Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data

4. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

5. DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

6. SGDR: Stochastic Gradient Descent with Warm Restarts

7. Wikipedia Bibliography:

   1. Kaggle

   2. Makridakis Competitions

   3. https://en.wikipedia.org/wiki/Walmart  :

      https://en.wikipedia.org/wiki/Walmart

   4. Exponential smoothing

   5. LightGBM

   6. https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting  :

      https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting

   7. Loss function

   8. https://en.wikipedia.org/wiki/Kyung_Hee_University  :

      https://en.wikipedia.org/wiki/Kyung_Hee_University

   9. https://en.wikipedia.org/wiki/Early_stopping  :

      https://en.wikipedia.org/wiki/Early_stopping

   10. https://en.wikipedia.org/wiki/Tweedie_distribution  :

       https://en.wikipedia.org/wiki/Tweedie_distribution

   11. https://en.wikipedia.org/wiki/Cross-validation_(statistics)  :

       https://en.wikipedia.org/wiki/Cross-validation_(statistics)

   12. Long short-term memory

   13. Dilution (neural networks)

   14. https://en.wikipedia.org/wiki/Poisson_regression  :

       https://en.wikipedia.org/wiki/Poisson_regression

   15. Autoregressive integrated moving average