容量与鲁棒性的权衡：重新审视多元时间序列预测中的通道独立策略
The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecasting
AI总结：
本文通过实证与理论分析揭示了多元时间序列预测中通道独立策略优于通道依赖策略的原因，指出CD容量高但鲁棒性差，CI以容量换鲁棒性，并提出PRReg方法以超越CI策略。
摘要：
多元时间序列数据由多个变量通道组成。多元预测模型需要捕捉通道之间的关系，以准确预测未来值。然而，近期出现了一些采用通道独立（Channel Independent, CI）策略的方法。这些方法将多元时间序列数据视为相互独立的单变量时间序列，并忽略通道之间的相关性。令人惊讶的是，我们的实证结果表明，使用CI策略训练的模型通常以显著优势优于使用通道依赖（Channel Dependent, CD）策略训练的模型。然而，文献中尚未对这一现象背后的原因进行深入探讨。本文对多元时间序列数据集的特征以及CI/CD策略进行了全面的实证和理论分析。我们的结果得出结论：CD方法具有更高的容量，但在准确预测分布漂移的时间序列方面往往缺乏鲁棒性。相比之下，CI方法以容量换取鲁棒的预测能力。受这些分析启发，我们提出了解决容量与鲁棒性困境的实用措施，包括一种改进的CD方法，称为带正则化的残差预测（Predict Residuals with Regularization, PRReg），该方法能够超越CI策略。我们希望我们的发现能够提高研究者对多元时间序列特征的认识，并启发构建更好的预测模型。
英文摘要：
Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between the channels to accurately predict future values. However, recently, there has been an emergence of methods that employ the Channel Independent (CI) strategy. These methods view multivariate time series data as separate univariate time series and disregard the correlation between channels. Surprisingly, our empirical results have shown that models trained with the CI strategy outperform those trained with the Channel Dependent (CD) strategy, usually by a significant margin. Nevertheless, the reasons behind this phenomenon have not yet been thoroughly explored in the literature. This paper provides comprehensive empirical and theoretical analyses of the characteristics of multivariate time series datasets and the CI/CD strategy. Our results conclude that the CD approach has higher capacity but often lacks robustness to accurately predict distributionally drifted time series. In contrast, the CI approach trades capacity for robust prediction. Practical measures inspired by these analyses are proposed to address the capacity and robustness dilemma, including a modified CD method called Predict Residuals with Regularization (PRReg) that can surpass the CI strategy. We hope our findings can raise awareness among researchers about the characteristics of multivariate time series and inspire the construction of better forecasting models.
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。
