TriTS：从多模态视角进行时间序列预测
TriTS: Time Series Forecasting from a Multimodal Perspective
AI总结：
TriTS从多模态视角提出新的跨模态解耦框架，通过时间、频率和2D视觉空间投影，结合周期感知重塑策略和视觉Mamba，实现高效的时间序列预测，实验表明其在多个基准数据集上达到SOTA性能。
摘要：
时间序列预测在金融、能源、交通和气象等关键领域中发挥着关键作用。然而，长期时间序列预测（LTSF）仍是一个重大挑战，因为现实世界的信号包含高度交织的时间动态，难以从纯1D视角完全捕捉。为打破这一表示瓶颈，我们提出TriTS，一种新颖的跨模态解耦框架，将1D时间序列投影到正交的时间、频率和2D视觉空间中。为了无缝弥合1D到2D模态的差距，而无需Vision Transformers（ViTs）带来的O(N²)计算开销，我们引入了周期感知重塑策略，并整合了视觉Mamba（Vim）。这种方法能够高效地建模跨周期依赖性作为全局视觉纹理，同时保持线性计算复杂度。此外，我们设计了多分辨率小波混合（MR-WM）模块用于频率模态，将非平稳信号显式解耦为趋势和噪声成分，以实现精细的时间-频率定位。最后，在时间域中保留了一个流式线性分支以确保数值稳定性。通过动态融合这三种互补的表示，TriTS能够有效适应多样化数据上下文。在多个基准数据集上的广泛实验表明，TriTS在多个基准数据集上达到了最先进的性能，从根本上超越了现有的基于视觉的预测器，通过大幅减少参数数量和推理延迟。
英文摘要：
Time series forecasting plays a pivotal role in critical sectors such as finance, energy, transportation, and meteorology. However, Long-term Time Series Forecasting (LTSF) remains a significant challenge because real-world signals contain highly entangled temporal dynamics that are difficult to fully capture from a purely 1D perspective. To break this representation bottleneck, we propose TriTS, a novel cross-modal disentanglement framework that projects 1D time series into orthogonal time, frequency, and 2D-vision spaces.To seamlessly bridge the 1D-to-2D modality gap without the prohibitive $O(N^2)$ computational overhead of Vision Transformers (ViTs), we introduce a Period-Aware Reshaping strategy and incorporate Visual Mamba (Vim). This approach efficiently models cross-period dependencies as global visual textures while maintaining linear computational complexity. Complementing this, we design a Multi-Resolution Wavelet Mixing (MR-WM) module for the frequency modality, which explicitly decouples non-stationary signals into trend and noise components to achieve fine-grained time-frequency localization. Finally, a streaming linear branch is retained in the time domain to anchor numerical stability. By dynamically fusing these three complementary representations, TriTS effectively adapts to diverse data contexts. Extensive experiments across multiple benchmark datasets demonstrate that TriTS achieves state-of-the-art (SOTA) performance, fundamentally outperforming existing vision-based forecasters by drastically reducing both parameter count and inference latency.
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。
