IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yijie Wang , Xiao Wu , Jiaying Zhang , Weiping Wang , Linjiang Zheng , Jiaxing Shang
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引用次数: 0

摘要

多变量时间序列预测(MTSF)被广泛应用于天气预报等研究密集型领域。最近,基于 Transformer 的模型受益于其自我关注机制,在实现 SOTA 性能方面表现突出。然而,现有模型在捕捉多变量相互依赖关系和局部语义表征方面存在不足。针对上述局限性,我们提出了一种基于序列聚类和动态周期修补的 Transformer 模型,命名为 CMDPPformer,该模型有两个显著特点:(1)提出了一种基于序列聚类的通道混合模块,它可以加强序列相似度高的变量之间的关联,削弱不相关变量的影响。具体来说,我们利用全时间序列聚类将多变量时间序列划分为若干个簇。之后,同一聚类中的变量共享同一 Transformer 主干网,而不同聚类中的变量互不影响。(2) 引入动态周期性修补模块,更好地捕捉语义信息,改进 Transformer 的局部语义表示。具体来说,将聚类后的多变量时间序列动态分割成周期性补丁,作为 Transformer 的输入标记。实验结果表明,与基于 SOTA Transformer 的模型相比,CMDPPformer 在能源、天气、疾病和经济等四个真实世界应用的七个基准测试中分别实现了 13.76% 和 10.16% 的总体相对改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Series clustering and dynamic periodic patching-based transformer for multivariate time series forecasting
Multivariate time series forecasting (MTSF) is widely employed in research-intensive domains, such as weather forecasting. Recently, Transformer-based models have outstanding ability to achieve SOTA performance, benefiting from its self-attention mechanism. However, existing models fall short in capturing multivariate inter-dependencies and local semantic representations. To tackle the above limitations, we propose a series clustering and dynamic periodic patching-based Transformer model named CMDPPformer, with two distinctive characteristics: (1) A channel-mixing module based on series clustering is proposed which can strengthen the association between variables with high sequence similarity, and weaken the effect of uncorrelated variables. Concretely, we use whole-time series clustering to group multivariate time series into clusters. After that, variables in the same cluster share the same Transformer backbone while variables in different clusters do not affect each other. (2) A dynamic periodic patching module is introduced which can better capture semantic information and improve Transformer’s local semantic representation. Concretely, multivariate time series after clustering are dynamically segmented into periodic patches as Transformer’s input token. Experimental results show that CMDPPformer can achieve an overall 13.76% and 10.16% relative improvements than SOTA Transformer-based models on seven benchmarks, covering four real-world applications: energy, weather, illness and economic.
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来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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