用于多元时间序列预测的联合时频域Transformer
A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting
AI总结：
本文提出联合时频域Transformer（JTFT），结合频域稀疏多尺度表示与固定长度时域表示，并引入低秩注意力层，在保持线性计算复杂度的同时提升多元长期时间序列预测性能。
摘要：
为了在降低计算需求的同时提升Transformer模型在长期多元预测中的性能，本文提出了联合时频域Transformer（JTFT）。JTFT结合时域和频域表示进行预测。频域表示通过利用少量可学习频率，在保持稀疏性的同时高效提取多尺度依赖关系。同时，时域（TD）表示由固定数量的最近数据点导出，增强了局部关系建模并减轻非平稳性的影响。重要的是，表示的长度与输入序列长度无关，使JTFT能够实现线性计算复杂度。此外，提出了一种低秩注意力层，以高效捕获跨维度依赖关系，从而防止因时间建模与通道建模相互纠缠而导致的性能下降。在六个真实世界数据集上的实验结果表明，JTFT在预测性能上优于最先进的基线方法。
英文摘要：
In order to enhance the performance of Transformer models for long-term multivariate forecasting while minimizing computational demands, this paper introduces the Joint Time-Frequency Domain Transformer (JTFT). JTFT combines time and frequency domain representations to make predictions. The frequency domain representation efficiently extracts multi-scale dependencies while maintaining sparsity by utilizing a small number of learnable frequencies. Simultaneously, the time domain (TD) representation is derived from a fixed number of the most recent data points, strengthening the modeling of local relationships and mitigating the effects of non-stationarity. Importantly, the length of the representation remains independent of the input sequence length, enabling JTFT to achieve linear computational complexity. Furthermore, a low-rank attention layer is proposed to efficiently capture cross-dimensional dependencies, thus preventing performance degradation resulting from the entanglement of temporal and channel-wise modeling. Experimental results on six real-world datasets demonstrate that JTFT outperforms state-of-the-art baselines in predictive performance.
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。
