Adapformer：面向多变量时间序列预测的自适应通道管理方法
Adapformer: Adaptive Channel Management for Multivariate Time Series Forecasting
AI总结：
针对多变量时间序列预测中现有通道独立、通道依赖策略的固有缺陷，本文提出Adapformer框架，通过双阶段自适应通道模块融合两类策略优势，实现了更优的预测精度与计算效率。
摘要：
在多变量时间序列预测（MTSF）任务中，受传统方法固有局限影响，精准建模多变量间的复杂依赖关系仍是一项重大挑战。现有多数模型要么采用通道独立（CI）策略，要么采用通道依赖（CD）策略，两类方法均存在明显缺陷：CI方法无法利用通道间交互的潜在信息，导致模型难以充分挖掘数据中隐含的统计依赖关系；与之相反，CD方法往往引入过多无关信息，存在模型过拟合、预测效率低下的风险。为解决上述问题，本文提出自适应预测Transformer（Adapformer），这是一种基于Transformer的先进框架，通过高效的通道管理融合CI与CD方法的优势。Adapformer的核心是双阶段编解码架构，包含用于优化嵌入过程的自适应通道增强模块（ACE），以及用于细化预测结果的自适应通道预测模块（ACF）：ACE通过选择性融合关键依赖增强token表征，ACF则通过聚焦最相关的协变量简化解码过程，大幅降低噪声与冗余。在多类数据集上的严格测试表明，Adapformer性能优于现有模型，同时提升了预测精度与计算效率，是当前MTSF领域的最优模型。
英文摘要：
In multivariate time series forecasting (MTSF), accurately modeling the intricate dependencies among multiple variables remains a significant challenge due to the inherent limitations of traditional approaches. Most existing models adopt either \textbf{channel-independent} (CI) or \textbf{channel-dependent} (CD) strategies, each presenting distinct drawbacks. CI methods fail to leverage the potential insights from inter-channel interactions, resulting in models that may not fully exploit the underlying statistical dependencies present in the data. Conversely, CD approaches often incorporate too much extraneous information, risking model overfitting and predictive inefficiency. To address these issues, we introduce the Adaptive Forecasting Transformer (\textbf{Adapformer}), an advanced Transformer-based framework that merges the benefits of CI and CD methodologies through effective channel management. The core of Adapformer lies in its dual-stage encoder-decoder architecture, which includes the \textbf{A}daptive \textbf{C}hannel \textbf{E}nhancer (\textbf{ACE}) for enriching embedding processes and the \textbf{A}daptive \textbf{C}hannel \textbf{F}orecaster (\textbf{ACF}) for refining the predictions. ACE enhances token representations by selectively incorporating essential dependencies, while ACF streamlines the decoding process by focusing on the most relevant covariates, substantially reducing noise and redundancy. Our rigorous testing on diverse datasets shows that Adapformer achieves superior performance over existing models, enhancing both predictive accuracy and computational efficiency, thus making it state-of-the-art in MTSF.
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。
