FCDNet：面向多元时间序列预测的频率引导互补依赖建模
FCDNet: Frequency-Guided Complementary Dependency Modeling for Multivariate Time-Series Forecasting
AI总结：
FCDNet利用频率引导的互补依赖建模，通过轻量级依赖构建器自适应提取长期与短期依赖，实现多元时间序列预测，在六个真实数据集上平均提升MAE 6.82%、RMSE 4.98%、MAPE 4.91%。
摘要：
多元时间序列（MTS）预测是许多真实世界非平稳动态场景中的一项具有挑战性的任务。除了序列内部的时间信号外，序列间的依赖关系在塑造未来趋势中也起着至关重要的作用。如何使模型能够感知依赖信息已引起大量研究关注。以往的方法要么基于领域知识预设依赖约束，要么利用实时特征相似性施加依赖约束。然而，MTS数据通常同时表现出持久的长期静态关系和短暂的短期交互，这两者相互影响其演化状态。为了更准确的MTS预测，有必要识别并纳入互补的依赖关系。时间序列中的频率信息反映了复杂时间动态背后的演化规律，不同的频率分量可用于很好地构建变量之间的长期和短期交互依赖结构。为此，我们提出了FCDNet，一个简洁而有效的多元时间序列预测框架。具体而言，FCDNet通过应用两个轻量级依赖构建器，从多级频率模式中自适应地提取长期和短期依赖信息，从而克服了上述限制。随着输入变量的增长，FCDNet中可训练参数的数量仅线性增加，这有利于模型的可扩展性并避免过拟合。此外，采用基于频率的视角可以有效减轻MTS数据中噪声的影响，有助于捕获更真实的依赖关系。在来自多个领域的六个真实世界数据集上的实验结果表明，FCDNet显著超过了强基线，在MAE上平均提升6.82%，在RMSE上平均提升4.98%，在MAPE上平均提升4.91%。
英文摘要：
Multivariate time-series (MTS) forecasting is a challenging task in many real-world non-stationary dynamic scenarios. In addition to intra-series temporal signals, the inter-series dependency also plays a crucial role in shaping future trends. How to enable the model's awareness of dependency information has raised substantial research attention. Previous approaches have either presupposed dependency constraints based on domain knowledge or imposed them using real-time feature similarity. However, MTS data often exhibit both enduring long-term static relationships and transient short-term interactions, which mutually influence their evolving states. It is necessary to recognize and incorporate the complementary dependencies for more accurate MTS prediction. The frequency information in time series reflects the evolutionary rules behind complex temporal dynamics, and different frequency components can be used to well construct long-term and short-term interactive dependency structures between variables. To this end, we propose FCDNet, a concise yet effective framework for multivariate time-series forecasting. Specifically, FCDNet overcomes the above limitations by applying two light-weight dependency constructors to help extract long- and short-term dependency information adaptively from multi-level frequency patterns. With the growth of input variables, the number of trainable parameters in FCDNet only increases linearly, which is conducive to the model's scalability and avoids over-fitting. Additionally, adopting a frequency-based perspective can effectively mitigate the influence of noise within MTS data, which helps capture more genuine dependencies. The experimental results on six real-world datasets from multiple fields show that FCDNet significantly exceeds strong baselines, with an average improvement of 6.82% on MAE, 4.98% on RMSE, and 4.91% on MAPE.
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。
