提出分离时间与变量建模的InvDec，显著提升高维时序预测性能。
InvDec: Inverted Decoder for Multivariate Time Series Forecasting with Separated Temporal and Variate Modeling
- 用倒置解码器在变量维度做自注意力，分离时间与变量建模。
- 在321变量电力数据上降低20.9%误差，高维数据优势明显。
- 适合高维多变量时序预测，尤其变量数多时效果更佳。
多变量时间序列预测需同时建模时间模式与变量间依赖。通道独立方法如PatchTST擅长时间建模但忽略变量相关性，纯变量注意力方法如iTransformer则牺牲时间编码。本文提出InvDec（倒置解码器），通过分层设计实现时间编码与变量级解码的原理性分离。其结合基于分块的时间编码器与在变量维度运行的倒置解码器，采用延迟变量嵌入，在完成时间编码后才引入变量特异性表示，保持时间特征完整性。自适应残差融合机制动态平衡不同维度数据集中的时间与变量信息。以PatchTST为基础构建InvDec-PatchTST。在七个基准测试上广泛实验表明：在高维数据上显著提升——电力数据（321变量）MSE降低20.9%，气象数据提升4.3%，交通数据提升2.7%，同时在低维ETT数据集上保持竞争力。消融实验证明各组件有效性，分析显示其优势随数据维度增加而增强，证实变量间建模在变量增多时愈发关键。
原文摘要 · Abstract (English)
Multivariate time series forecasting requires simultaneously modeling temporal patterns and cross-variate dependencies. Channel-independent methods such as PatchTST excel at temporal modeling but ignore variable correlations, while pure variate-attention approaches such as iTransformer sacrifice temporal encoding. We proposeInvDec (Inverted Decoder), a hybrid architecture that achieves principled separation between temporal encoding and variate-level decoding. InvDec combines a patch-based temporal encoder with an inverted decoder operating on the variate dimension through variate-wise self-attention. We introduce delayed variate embeddings that enrich variable-specific representations only after temporal encoding, preserving temporal feature integrity. An adaptive residual fusion mechanism dynamically balances temporal and variate information across datasets of varying dimensions. Instantiating InvDec with PatchTST yields InvDec-PatchTST. Extensive experiments on seven benchmarks demonstrate significant gains on high-dimensional datasets: 20.9% MSE reduction on Electricity (321 variables), 4.3% improvement on Weather, and 2.7% gain on Traffic compared to PatchTST, while maintaining competitive performance on low-dimensional ETT datasets. Ablation studies validate each component, and analysis reveals that InvDec's advantage grows with dataset dimensionality, confirming that cross-variate modeling becomes critical as the number of variables increases.
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