Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods

Author:

Wellens Arnoud P.,Udenio MaxiORCID,Boute Robert N.ORCID

Publisher

Elsevier BV

Subject

Business and International Management

Reference42 articles.

1. A data-driven framework to new product demand prediction: Integrating product differentiation and transfer learning approach;Afrin;Expert Systems with Applications,2018

2. Selecting rows and columns for training support vector regression models with large retail datasets;Ali;European Journal of Operational Research,2013

3. Tree-based methods for clustering time series using domain-relevant attributes;Ashouri;Journal of Business Analytics,2019

4. The theta model: A decomposition approach to forecasting;Assimakopoulos;International Journal of Forecasting,2000

5. Improving the accuracy of global forecasting models using time series data augmentation;Bandara,2020

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1. Pooling and Boosting for Demand Prediction in Retail: A Transfer Learning Approach;Manufacturing & Service Operations Management;2024-05-30

2. Optimizing inventory control through a data-driven and model-independent framework;EURO Journal on Transportation and Logistics;2023

3. Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future;Palgrave Advances in the Economics of Innovation and Technology;2023

4. Responses to the discussions and commentaries of the M5 Special Issue;International Journal of Forecasting;2022-10

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