面向长序列时间序列预测的双相关分布特征提取网络

IF 6.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jin Fan , Zehao Wang , Feiwei Qin , Huifeng Wu , Danfeng Sun , Jia Wu
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引用次数: 0

摘要

长序列时间序列预测(LSTF)具有广泛的实际应用,许多方法推动了该领域的重大进展。然而,挑战仍然存在,例如处理分布变化,从全球和本地角度捕捉特征,如时间序列的趋势和季节性变化。为了解决这些问题,我们提出了一个自动/相互关联分布特征提取网络(ACDN),这是一个线性复杂性模型,它集成了两个关键模块和一种机制:分布处理模块对输入序列进行归一化,并动态预测预测序列的分布,以捕获分布移位的特征。自动/相互关联模块通过计算单个时间序列片段的自相关和不同片段之间的相互关联来捕获不断变化的趋势分量。细微特征保留机制补偿了由于编码器-解码器结构中的维数降低而导致的特征损失,确保保留关键的细粒度模式。在不同领域的9个数据集上进行的大量实验证明了ACDN在多元LSTF任务中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A distribution feature extracting network with dual correlation for long sequence time-series forecasting
Long-sequence time-series forecasting (LSTF) has broad real-world applications, and numerous methods have driven significant advancements in this field. However, challenges remain, such as dealing with distribution shifts and capturing the features from both global and local perspectives, such as trends and seasonal changes in time series. To address these issues, we propose an Auto/Cross-correlation Distribution Feature Extraction Network (ACDN), a linear complexity model that integrates two key modules and a mechanism: the Distribution Processing Module normalizes the input sequence and dynamically predicts the distribution of the forecasted sequence to capture the features of distribution shifts. The Auto/Cross-Correlation Module captures evolving trend components by computing both the autocorrelation of individual time series segments and the cross-correlation between different segments. The Subtle Feature Preservation Mechanism compensates for feature loss caused by dimensionality reduction in the encoder–decoder structure, ensuring critical fine-grained patterns are retained. Extensive experiments on nine datasets from diverse domains demonstrate the effectiveness of ACDN in multivariate LSTF tasks.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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