Inter-Series Transformer：在时间序列预测中关注产品间关系
Inter-Series Transformer: Attending to Products in Time Series Forecasting
AI总结：
本文提出Inter-Series Transformer，通过跨时间序列注意力和共享多任务网络捕捉产品间交互并缓解稀疏性，在医疗设备供应链案例及公开数据集上提升需求预测性能。
摘要：
时间序列预测是从供应链管理到天气预报等众多领域中的一项重要任务。近年来，Transformer神经网络架构在常用时间序列基准数据集上的预测已展现出良好效果。然而，其在供应链需求预测中的应用仍然有限，因为这类预测可能具有稀疏性和跨序列效应等颇具挑战性的特征。在本研究中，我们探索基于Transformer的模型在供应链需求预测中的应用。具体而言，我们开发了一种新的基于Transformer的预测方法，该方法采用共享的、多任务的逐时间序列网络，并用一个初始组件在不同时间序列之间施加注意力，以捕捉相互作用并帮助缓解稀疏性问题。我们提供了一个案例研究，将该方法应用于一家医疗器械制造公司，并成功改善了其需求预测。为进一步验证该方法，我们还将其应用于公开需求预测数据集；结果表明，在私有和公开数据集上，与多种基线方法及最先进预测方法相比，该方法取得了具有竞争力甚至更优的性能。
英文摘要：
Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have shown promising results in forecasting on common time series benchmark datasets. However, application to supply chain demand forecasting, which can have challenging characteristics such as sparsity and cross-series effects, has been limited. In this work, we explore the application of Transformer-based models to supply chain demand forecasting. In particular, we develop a new Transformer-based forecasting approach using a shared, multi-task per-time series network with an initial component applying attention across time series, to capture interactions and help address sparsity. We provide a case study applying our approach to successfully improve demand prediction for a medical device manufacturing company. To further validate our approach, we also apply it to public demand forecasting datasets as well and demonstrate competitive to superior performance compared to a variety of baseline and state-of-the-art forecast methods across the private and public datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
