不一致多元时间序列预测

IF 10.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Li Shen;Yangzhu Wang;Xuyi Fan;Xu Yang;Huaxin Qiu
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引用次数: 0

摘要

传统的统计时间序列预测模型依赖于模型识别方法来识别最有价值的模型变量进行研究;因此,模型参数随滚动窗的统计特征而变化,以达到最优性。目前,尽管基于深度学习的方法取得了很好的多元预测性能,但无论观察到的局部时间序列性质和动态交叉变量关系如何,它们对变量相关性的表示都是一致的,这使得它们容易出现过拟合。为了弥补这一差距,我们提出了一种新的不一致时间序列预测变压器FPPformer-MD。FPPformer-MD利用多分辨率分析将每个单变量序列转换为多个频率尺度,并通过其方差评估局部变量的相关性。因此,FPPformer-MD获得了更丰富的输入特征,其内部的不一致交叉变量注意机制使得交叉变量特征的自适应提取成为可能。为了进一步缓解过拟合问题,我们应用动态模态分解进行交叉变量数据增强,在模型训练过程中用其他相关序列重建序列离群值。在13个实际基准上进行的大量实验证明了FPPformer-MD的最先进性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inconsistent Multivariate Time Series Forecasting
Traditional statistical time series forecasting models rely on model identification methods to identify the worthiest model variants to investigate; therefore, the model parameters change with the statistical features of rolling windows to reach optimality. Currently, although deep-learning-based methods achieve promising multivariate forecasting performance, their representations of variable correlations are consistent regardless of the observed local time series properties and dynamic cross-variable relations, rendering them prone to overfitting. To bridge this gap, we propose FPPformer-MD, a novel inconsistent time series forecasting transformer. FPPformer-MD leverages multiresolution analysis to transform each univariate series into multiple frequency scales and evaluate the local variable correlations via their variances. Thus, FPPformer-MD receives richer input features, and its inner inconsistent cross-variable attention mechanism enables the adaptive extraction of cross-variable features. To further alleviate the overfitting problem, we apply dynamic mode decomposition to perform cross-variable data augmentation, which reconstructs the sequence outliers with other correlated sequences during the model training process. Extensive experiments conducted on thirteen real-world benchmarks demonstrate the state-of-the-art performance of FPPformer-MD.
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
期刊介绍: The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.
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