Python framework for a good neural network for the Makidrakis 5 (M5) competition hosted on Kaggle.
-
Updated
May 21, 2020 - Python
#
m5-competition
Here are 10 public repositories matching this topic...
Time series forecasting of retail items
timeseries forecasting walmart m5-competition m5-forecasting m5-forecasting-accuracy hierarchicaltimeseries
-
Updated
Jan 17, 2022 - Jupyter Notebook
Forest the unit sales of Walmart retail goods for the next 28 days.
-
Updated
Dec 8, 2020 - Jupyter Notebook
This repo contains the R language code used for m5 forecasting accuracy. The models used for forecasting are Xgboost, Catboost, Lightgbm and facebook prophet
-
Updated
Jun 30, 2020 - Jupyter Notebook
The goal of this project is to provide 28-days ahead point forecasts for 30490 various items sold by Walmart.
-
Updated
Sep 14, 2020 - Jupyter Notebook
Data for M5 Walmart Kaggle Competition
data-science kaggle-competition walmart kaggle-dataset time-series-forecasting walmart-sales-forecasting m5-competition m5-forecasting
-
Updated
Sep 7, 2022 - R
Meta-learnng solution using FFORMA for the M5 Uncertainty Forecasting Competition in Kaggle: https://www.kaggle.com/c/m5-forecasting-uncertainty
-
Updated
Mar 23, 2021 - Python
28-day demand forecasting on the Walmart M5 dataset: LightGBM, LSTM/GRU and Seq2Seq GRU scored with WRMSSE against a naive baseline.
-
Updated
Sep 5, 2026 - Python
Leakage-safe demand forecasting and inventory decision support on the M5 dataset (3,044 series): baselines vs XGBoost vs a time-series foundation model.
python xgboost demand-forecasting time-series-forecasting mlops inventory-optimization m5-competition
-
Updated
Sep 15, 2026 - Python
Add this topic to your repo
To associate your repository with the m5-competition topic, visit your repo's landing page and select "manage topics."