时间查询网络用于高效多元时间序列预测
Temporal Query Network for Efficient Multivariate Time Series Forecasting
AI总结：
提出时间查询网络（TQNet），通过周期性移位可学习向量作为注意力查询捕获全局变量相关性，仅用单层注意力和轻量MLP，在12个数据集上达到最先进精度且效率高。
摘要：
充分建模变量（即通道）之间的相关性对于实现准确的多元时间序列预测（MTSF）至关重要。本文提出了一种名为时间查询（TQ）的新技术，以更有效地捕获多元相关性，从而提高MTSF任务中的模型性能。技术上，TQ技术采用周期性移位的可学习向量作为注意力机制中的查询，以捕获全局变量间模式，而键和值则来自原始输入数据，以编码局部、样本级别的相关性。基于TQ技术，我们开发了一个简单而高效的模型，名为时间查询网络（TQNet），该模型仅采用单层注意力机制和一个轻量级多层感知器（MLP）。大量实验表明，TQNet学习了更稳健的多元相关性，在12个具有挑战性的真实世界数据集上实现了最先进的预测精度。此外，即使在高维数据集上，TQNet也实现了与基于线性方法相当的高效率，平衡了性能和计算成本。代码可在以下网址获取：https://github.com/ACAT-SCUT/TQNet。
英文摘要：
Sufficiently modeling the correlations among variables (aka channels) is crucial for achieving accurate multivariate time series forecasting (MTSF). In this paper, we propose a novel technique called Temporal Query (TQ) to more effectively capture multivariate correlations, thereby improving model performance in MTSF tasks. Technically, the TQ technique employs periodically shifted learnable vectors as queries in the attention mechanism to capture global inter-variable patterns, while the keys and values are derived from the raw input data to encode local, sample-level correlations. Building upon the TQ technique, we develop a simple yet efficient model named Temporal Query Network (TQNet), which employs only a single-layer attention mechanism and a lightweight multi-layer perceptron (MLP). Extensive experiments demonstrate that TQNet learns more robust multivariate correlations, achieving state-of-the-art forecasting accuracy across 12 challenging real-world datasets. Furthermore, TQNet achieves high efficiency comparable to linear-based methods even on high-dimensional datasets, balancing performance and computational cost. The code is available at: https://github.com/ACAT-SCUT/TQNet.
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。
