提出新型线性模型，高效分离时间与频率特征，提升长期时序预测性能。
Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting
- 将单一权重矩阵拆分为专注频、时序和独立频段处理的模块
- 参数量从二次方降至线性，计算复杂度降为对数线性，保持高精度
- 适合追求轻量化与高效率的工业级时序预测场景
长期时序预测（LTSF）在多个领域至关重要，但诸如Transformer等复杂深度模型在长序列上易过拟合。线性全连接模型因其参数少、表现佳而成为有力替代方案，但其依赖单一全局权重矩阵，导致参数呈二次冗余，并纠缠时间与频率特性。为此，我们提出DiPE-Linear，通过三个核心组件实现解耦：静态频段注意力聚焦关键频率，静态时序注意力关注关键时间步，独立频段映射独立处理各频率成分。结合低秩权重共享策略，有效提升多变量数据效率。该解耦架构使参数复杂度由二次降至线性，计算复杂度降为对数线性。在真实数据集上的实验表明，DiPE-Linear以显著更少参数达到领先性能，为LTSF建立新的高效基准。代码已开源。
原文摘要 · Abstract (English)
Long-term Time Series Forecasting (LTSF) is crucial across various domains, but complex deep models like Transformers are often prone to overfitting on extended sequences. Linear Fully Connected models have emerged as a powerful alternative, achieving competitive results with fewer parameters. However, their reliance on a single, monolithic weight matrix leads to quadratic parameter redundancy and an entanglement of temporal and frequential properties. To address this, we propose DiPE-Linear, a novel model that disentangles this monolithic mapping into a sequence of specialized, parameter-efficient modules. DiPE-Linear features three core components: Static Frequential Attention to prioritize critical frequencies, Static Time Attention to focus on key time steps, and Independent Frequential Mapping to independently process frequency components. A Low-rank Weight Sharing policy further enhances efficiency for multivariate data. This disentangled architecture collectively reduces parameter complexity from quadratic to linear and computational complexity to log-linear. Experiments on real-world datasets show that DiPE-Linear delivers state-of-the-art performance with significantly fewer parameters, establishing a new and highly efficient baseline for LTSF. Our code is available at https://github.com/wintertee/DiPE-Linear/
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