文献类型：期刊文献

英文题名：A truncated Gaussian distribution based multi-scale segment-wise fusion transformer model for multi-step commodity price forecasting

作者：Peng, Xin[1];Chen, Zhengxiang[1];Zhang, Jiale[1];Li, Zhi[1];Du, Wenli[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2024

卷号：133

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20241715968629);WOS:【SCI-EXPANDED(收录号:WOS:001218760200002)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , National Natural Science Foundation of China (62173145, 62303186) , the Shanghai Committee of Science and Technology, China (Grant No. 22DZ1101500) and Shanghai AI lab.

语种：英文

外文关键词：Commodity price forecasting; Multi-step prediction; Truncated Gaussian distribution; Time-varying trend characteristics; Price uncertainty estimation

摘要：Accurately forecasting commodity price trends is crucial for producers, market participants, and related enterprises to make informed decisions regarding production planning and scheduling. However, achieving high accuracy in multi -step forecasting poses significant challenges due to the unique financial characteristics inherent in commodities. Thus, this paper proposes a novel truncated Gaussian distribution based multi -scale segment -wise fusion Transformer for multi -step commodity price forecasting. First, a multi -scale segment -wise fusion module, which capture the time dependencies from different time granularity, is designed to describe the time -varying trend characteristics of commodity prices. Second, considering the characteristics of price range fluctuation and truncation, a truncated Gaussian distribution is introduced to describe price uncertainty. Last, to evaluate the proposed method's effectiveness, extensive experiments are conducted using real data on energy chemical product prices. The experimental results demonstrate that the proposed method accurately captures price change trends and effectively estimates price uncertainty. Compared to the widely adopted Autoformer, our approach achieves approximately 30% reductions in both root mean square error (RMSE) and mean absolute error (MAE) metrics. Additionally, it exhibits certain advantages over the current state-ofthe-art (SOTA). In the 20 -step and 60 -step multi -step prediction tasks, the proposed method achieves RMSE values of 91.18 and 142.94, respectively, surpassing the current SOTA. The introduced research framework provides valuable insights for decision -makers engaged in analyzing and forecasting commodity markets. The code is available on https://github.com/dean-ob/TGD-MSSF.