Prediction of customer demand for perishable products in retail inventory management, using the hybrid prophet-XGBoost model during the post-COVID-19 period

This publication has 13 references indexed in Scilit:

• Time-Series Forecasting of Seasonal Data Using Machine Learning Methods
  Algorithms, 2023
• Forecasting Sales Profiles of Products in an Exceptional Context: COVID-19 Pandemic
  International Journal of Computational Intelligence Systems, 2022
• Strain prediction for historical timber buildings with a hybrid Prophet-XGBoost model
  Mechanical Systems and Signal Processing, 2022
• hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost
  Biomedical Signal Processing and Control, 2022
• Forecasting: theory and practice
  International Journal of Forecasting, 2022
• A Deep Learning‐Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection
  Wireless Communications and Mobile Computing, 2021
• The impact of the COVID public policies on the Chilean households
  Applied Economics Letters, 2020
• Forecasting at Scale
  The American Statistician, 2018
• XGBoost
  2016
• Demand seasonality in retail inventory management
  European Journal of Operational Research, 2014