多元时间序列预测的平稳性探索
Stationarity Exploration for Multivariate Time Series Forecasting
AI总结：
针对频域预测难以探索平稳信息的问题，提出振幅-相位重构网络APRNet，通过解耦信号特征并利用KLC模块灵活刻画平稳特征，显著增强了时变模式捕获能力，表现优于现有SOTA方法。
摘要：
基于深度学习的时间序列预测已得到广泛应用。近期，将时间序列数据转换到频域进行预测因能准确探索周期性模式而变得流行。然而，现有方法往往无法从复杂交织的频率分量中有效探索平稳性信息。在本文中，我们提出了一种简单而有效的振幅-相位重构网络（APRNet），该网络对振幅和相位的相互关系进行建模，防止振幅和相位受到不同物理量的约束，从而解耦信号的不同特征以捕获平稳性信息。具体而言，我们沿序列和通道维度表示多元时间序列输入，突出了在多个交互频率上振幅与相位之间的相关性。我们提出了一种新颖的基于 Kolmogorov-Arnold-Network 的局部相关性（KLC）模块，利用单变量函数自适应地拟合局部函数，从而能够更灵活地刻画不同振幅和相位下的平稳特征。这显著增强了模型捕获时变模式的能力。大量实验证明了我们的 APRNet 相对于最新技术（SOTAs）的优越性。
英文摘要：
Deep learning-based time series forecasting has found widespread applications. Recently, converting time series data into the frequency domain for forecasting has become popular for accurately exploring periodic patterns. However, existing methods often cannot effectively explore stationary information from complex intertwined frequency components. In this paper, we propose a simple yet effective Amplitude-Phase Reconstruct Network (APRNet) that models the inter-relationships of amplitude and phase, which prevents the amplitude and phase from being constrained by different physical quantities, thereby decoupling the distinct characteristics of signals for capturing stationary information. Specifically, we represent the multivariate time series input across sequence and channel dimensions, highlighting the correlation between amplitude and phase at multiple interaction frequencies. We propose a novel Kolmogorov-Arnold-Network-based Local Correlation (KLC) module to adaptively fit local functions using univariate functions, enabling more flexible characterization of stationary features across different amplitudes and phases. This significantly enhances the model's capability to capture time-varying patterns. Extensive experiments demonstrate the superiority of our APRNet against the state-of-the-arts (SOTAs).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
