It contains the code and data for M5 Forecasting - Accuracy competition on Kaggle. The details and data for this competition can be found here: https://www.kaggle.com/c/m5-forecasting-accuracy/overview

• In this solution, we have built different models for different (10) stores and different (4) weeks (1-7, 8-14, 15-21, 22-28), so we are building total 40 models for each train_train_day_x (different validation periods for robust evaluation and hyper-parameter tuning).
• Features used are as following:
  • General base features
  • General price based features
  • General calendar and time based features
  • Lag and rolling mean/std features
  • Target encoding features for categorical variables
• Lightgbm with tweedie loss is used for modeling.
• The more implementation details can be found here: https://www.kaggle.com/c/m5-forecasting-accuracy/discussion/163216

• This notebook explores how GroupKFold CV strategy in Sklearn can be used for hyper-parameter tuning for time-series data.
• In this notebook we haven't done any hyper-parameter tuning though, GroupKFold CV has just been used for validating the model's performance but the same methodology can be used for hyper-parameter tuning.
• You can learn more about GroupKFold CV and how it reduces the possibility of leakage with time-series CV from the Markdown section of the notebook.
• Custom objective function and validation metric are used which works as a proxy for WRMSSE, competition' evaluation metric.
• Lightgbm with regression (default) loss is used for modeling.
• The data for this notebook are available at:
  • https://www.kaggle.com/ragnar123/m5-reduce-data
  • https://www.kaggle.com/c/m5-forecasting-accuracy/data