@inproceedings{f225e99786f44d8eb2fc123d07c29827,
title = "A Multi-scale Feature Fusion Method for Demand Forecasting in Supply Chain Management",
abstract = "For data-driven supply chain product demand forecasting, traditional methods tend to oversimplify the complexities of supply chain data, often neglecting its inherent heterogeneity and failing to extract valuable features. In response, we introduce a novel approach called Multi-Scale Feature Fusion Approach with Attention Mechanisms (MO-MSA) for supply chain product demand forecasting. MO-MSA leverages the distinctive characteristics of historical supply chain data, conducts multiscale feature extraction on time series data, and employs a string-parallel hybrid multi-step forecasting strategy to significantly enhance forecasting accuracy. Our experimental validation on the Corporaci{\'o}n Favorita Grocery Sales Forecasting dataset demonstrates that MO-MSA outperforms various existing methods, delivering superior average forecasting.",
keywords = "Demand Forecasting, Multi-Scale Feature Fusion, Supply Chain Management",
author = "Jiajie Wu and Jin Cui and Mei Yuan and Xiaoyang Fan and Jing Zhang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 6th Conference on Intelligent Networked Things on Intelligent Networked Things, CINT 2024 ; Conference date: 18-05-2024 Through 18-05-2024",
year = "2024",
doi = "10.1007/978-981-97-3948-6\_10",
language = "英语",
isbn = "9789819739479",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "97--105",
editor = "Lin Zhang and Wensheng Yu and Quan Wang and Yuanjun Laili and Yongkui Liu",
booktitle = "Intelligent Networked Things - The 6th Conference on Intelligent Networked Things, CINT 2024, Proceedings",
address = "德国",
}