Prediction of Store Demands by Decision Trees and Recurrent Neural Networks Ensemble with Transfer Learning

Nikica Perić, Naomi-Frida Munitić, Ivana Bašljan, Vinko Lešić

2022

Abstract

Simple vehicle routing problem (VRP) algorithms today achieve near-optimal solution and solve problems with a large number of nodes. Recently, these algorithms are upgraded with additional constraints to respect an increasing number of real-world conditions and, further on, adding a predictive character to the optimization. A distinctive contribution lies in taking into account the predictions of orders that are yet to occur. Such problems fall under time series approaches that are most often obtained using statistical methods or historical data heuristics. Machine learning methods have proven to be superior to statistical methods in most of the literature. In this paper, machine learning techniques for predicting the mass of total daily orders for individual stores are further elaborated and tested on historical data of a local retail company. Among the tested methods are Gradient Boosting Decision Tree methods (XGBoost and LightGBM) and methods of Recurrent Neural Networks (LSTM, GRU and their variations using transfer learning). Finally, an ensemble of these methods is performed, which provides the highest prediction accuracy. The final models use the information on historical order quantities and time-related slack variables.

Paper Citation

in Harvard Style

Perić N., Munitić N., Bašljan I. and Lešić V. (2022). Prediction of Store Demands by Decision Trees and Recurrent Neural Networks Ensemble with Transfer Learning. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 218-225. DOI: 10.5220/0010802500003116

in Bibtex Style

@conference{icaart22,
author={Nikica Perić and Naomi-Frida Munitić and Ivana Bašljan and Vinko Lešić},
title={Prediction of Store Demands by Decision Trees and Recurrent Neural Networks Ensemble with Transfer Learning},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={218-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010802500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Prediction of Store Demands by Decision Trees and Recurrent Neural Networks Ensemble with Transfer Learning
SN - 978-989-758-547-0
AU - Perić N.
AU - Munitić N.
AU - Bašljan I.
AU - Lešić V.
PY - 2022
SP - 218
EP - 225
DO - 10.5220/0010802500003116