通过多频参考序列捕捉周期特征，实现高精度可扩展时间序列预测
MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting
- 基于频谱分析构建多频参考序列，用Transformer计算跨序列注意力
- 在多个公开和合成数据集上达到顶尖性能，优于现有方法
- 仅用少量参考序列即可实现高效计算，适合大规模时序场景
多变量时间序列预测在诸多领域具有重要价值，需有效捕捉复杂的时序与变量间动态。传统方法多聚焦网络结构设计以挖掘潜在关系或时序依赖，但难以揭示决定可预测性的内在规律。受信号分解启发，本文提出时间序列可预测性源于不同频率的周期特性。为此，我们提出一种基于多频参考序列相关分析的新方法：通过对长期训练数据进行频谱分析，识别主导频段及其谐波，构造基线模式参考序列；不同于信号分解将原始序列表示为基信号的线性组合，本方法利用Transformer计算原始序列与参考序列间的交叉注意力，提取关键预测特征。在主流公开及合成数据集上的实验表明，该方法达到当前最优性能。此外，通过聚焦少量参考序列的注意力机制而非变量对之间的注意力，显著提升模型可扩展性与适用范围。代码已开源：https://github.com/yuliang555/MFRS。
原文摘要 · Abstract (English)
Multivariate time-series forecasting holds immense value across diverse applications, requiring methods to effectively capture complex temporal and inter-variable dynamics. A key challenge lies in uncovering the intrinsic patterns that govern predictability, beyond conventional designs, focusing on network architectures to explore latent relationships or temporal dependencies. Inspired by signal decomposition, this paper posits that time series predictability is derived from periodic characteristics at different frequencies. Consequently, we propose a novel time series forecasting method based on multi-frequency reference series correlation analysis. Through spectral analysis on long-term training data, we identify dominant spectral components and their harmonics to design base-pattern reference series. Unlike signal decomposition, which represents the original series as a linear combination of basis signals, our method uses a transformer model to compute cross-attention between the original series and reference series, capturing essential features for forecasting. Experiments on major open and synthetic datasets show state-of-the-art performance. Furthermore, by focusing on attention with a small number of reference series rather than pairwise variable attention, our method ensures scalability and broad applicability. The source code is available at: https://github.com/yuliang555/MFRS
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