基于多粒度多尺度深度时空模型的长期序列预测方法  

Long-term Series Forecasting Method Based on Multi-granularity Multi-scale Deep Spatio-Temporal Modeling

作　　者：陈加毫 谢良 廖思灏 吴雨琛 徐海蛟 CHEN Jiahao;XIE Liang;LIAO Sihao;WU Yuchen;XU Haijiao(College of Science,Wuhan University of Technology,Wuhan 430070,China;School of Computer Science,Guangdong University of Education,Guangzhou 510303,China)

机构地区：[1]武汉理工大学理学院,武汉430070 [2]广东第二师范学院计算机学院,广州510303

出　　处：《计算机科学》2025年第2期91-98,共8页Computer Science

基　　金：广东省自然科学基金(2020A1515011208);广州市基础研究教计划基础与应用基础研究项目(202102080353);广东省普通高校自然科学类特色创新项目(2019KTSCX117)。

摘　　要：时间序列建模一直是金融和交通等多个领域研究的热点,时空模型因能更全面捕捉时序数据的复杂关联和趋势,受到研究者们的广泛关注。近年来,基于时空模型的长期序列预测取得显著成果,但现有方法受到多粒度或多尺度研究的限制,无法充分挖掘数据的时空信息。为解决这一问题,提出了一种多粒度多尺度深度时空模型(MMDSTM)。该模型首先通过分解初始数据获取季节、周期和粒度序列;然后,利用基于多尺度等距卷积生成尺度预测,利用基于注意力的时空特征层生成多粒度预测;最后,通过多层次融合合并多粒度与多尺度预测的预测结果。在实验中,MMDSTM相比近期的新方法在股票、交通和电池数据集上MSE指标分别下降了6.2%,21.5%和1%。多粒度和多尺度的引入显著提升了时间序列预测性能。Time series modeling has been the focus of research in a number of fields,including finance and transportation,and spatio-temporal models have received a lot of attention from researchers because of their ability to capture the complex associations and trends in time-series data more comprehensively.In recent years,long-term series forecasting based on spatio-temporal modeling has achieved remarkable results,but the existing methods are limited by multi-granularity or multi-scale studies,which cannot fully mine the spatio-temporal information of the data.To overcome this problem,a multi-granularity multi-scale deep spatio-temporal model(MMDSTM)is proposed.The model first obtains seasonal,periodic and granularity sequences by decomposing the initial data.Then,the multi-scale isometric convolution generates scale predictions,while attention-based spatio-temporal feature layers generates multi-granularity predictions.Finally,the prediction results of multi-granularity and multi-scale predictions are merged by multi-level fusion.In experiments,MMDSTM’s MSE metric decreases by 6.2%,21.5%and 1%compared to other methods on stock,traffic and battery datasets,and the introduction of multi-granularity and multi-scale significantly improves the time series forecasting performance.

关 键 词：多粒度学习 多尺度学习 时间序列预测 金融市场 交通流速度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]