复兴多元时间序列预测：基于序列间依赖与序列内变化建模的可学习分解
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
AI总结：
本文提出 Leddam 模型，通过可学习分解策略和双注意力模块同时建模序列间依赖与序列内变化，在八个数据集上显著提升多元时间序列预测性能，且分解策略可即插即用，使其他方法 MSE 降低 11.87% 至 48.56%。
摘要：
多元时间序列预测至关重要，要求对复杂模式进行精确建模，包括序列间依赖和序列内变化。每个时间序列中独特的趋势特征带来了挑战，而现有方法依赖基本的移动平均核，可能难以应对现实世界数据中的非线性结构和复杂趋势。鉴于此，我们引入了一种可学习的分解策略，以更合理地捕获动态趋势信息。此外，我们提出了一个双注意力模块，专门用于同时捕获序列间依赖和序列内变化，以实现更好的时间序列预测，该模块通过通道维自注意力和自回归自注意力来实现。为了评估我们方法的有效性，我们在八个开源数据集上进行了实验，并与最先进的方法进行了比较。通过比较结果，我们的 Leddam（可学习分解与双注意力模块）不仅在预测性能上展现出显著提升，而且所提出的分解策略可以插入其他方法中，带来大幅性能提升，MSE 误差降低幅度从 11.87% 到 48.56% 不等。
英文摘要：
Predicting multivariate time series is crucial, demanding precise modeling of intricate patterns, including inter-series dependencies and intra-series variations. Distinctive trend characteristics in each time series pose challenges, and existing methods, relying on basic moving average kernels, may struggle with the non-linear structure and complex trends in real-world data. Given that, we introduce a learnable decomposition strategy to capture dynamic trend information more reasonably. Additionally, we propose a dual attention module tailored to capture inter-series dependencies and intra-series variations simultaneously for better time series forecasting, which is implemented by channel-wise self-attention and autoregressive self-attention. To evaluate the effectiveness of our method, we conducted experiments across eight open-source datasets and compared it with the state-of-the-art methods. Through the comparison results, our Leddam (LEarnable Decomposition and Dual Attention Module) not only demonstrates significant advancements in predictive performance, but also the proposed decomposition strategy can be plugged into other methods with a large performance-boosting, from 11.87% to 48.56% MSE error degradation.
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。
