LightSAE：面向IoT多元时间序列预测的参数高效与异构感知嵌入
LightSAE: Parameter-Efficient and Heterogeneity-Aware Embedding for IoT Multivariate Time Series Forecasting
AI总结：
针对IoT多元时间序列预测中共享嵌入掩盖通道特定信息的问题，提出LightSAE模块，通过低秩分解与共享门控组件池实现参数高效的异构感知嵌入，在多数据集和架构上以极低参数开销显著提升预测精度。
摘要：
现代物联网系统生成海量异构多元时间序列数据。此类数据的准确多元时间序列预测（MTSF）对众多应用至关重要。然而，现有方法几乎普遍采用共享嵌入层，以相同方式处理所有通道，这造成了表示瓶颈，掩盖了有价值的通道特定信息。为应对此挑战，我们提出了共享辅助嵌入（SAE）框架，将嵌入分解为捕获共同模式的共享基础组件和对独特偏差进行建模的通道特定辅助组件。在此分解中，我们经验性地观察到，辅助组件倾向于表现出低秩和聚类特性，这种结构模式在使用纯独立嵌入时显著不明显。因此，我们设计了LightSAE，这是一个参数高效的嵌入模块，通过低秩分解和共享门控组件池来运用这些观察到的特性。在9个IoT相关数据集和4种骨干架构上的大量实验证明了LightSAE的有效性，仅增加4.0%的参数便实现了高达22.8%的MSE提升。
英文摘要：
Modern Internet of Things (IoT) systems generate massive, heterogeneous multivariate time series data. Accurate Multivariate Time Series Forecasting (MTSF) of such data is critical for numerous applications. However, existing methods almost universally employ a shared embedding layer that processes all channels identically, creating a representational bottleneck that obscures valuable channel-specific information. To address this challenge, we introduce a Shared-Auxiliary Embedding (SAE) framework that decomposes the embedding into a shared base component capturing common patterns and channel-specific auxiliary components modeling unique deviations. Within this decomposition, we \rev{empirically observe} that the auxiliary components tend to exhibit low-rank and clustering characteristics, a structural pattern that is significantly less apparent when using purely independent embeddings. Consequently, we design LightSAE, a parameter-efficient embedding module that operationalizes these observed characteristics through low-rank factorization and a shared, gated component pool. Extensive experiments across 9 IoT-related datasets and 4 backbone architectures demonstrate LightSAE's effectiveness, achieving MSE improvements of up to 22.8\% with only 4.0\% parameter increase.
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。
