PFformer：一种用于极端自适应多元时间序列预测的无位置Transformer变体
PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting
AI总结：
PFformer是一种无位置Transformer变体，通过EFE和AEE嵌入策略捕捉变量间依赖，在极端自适应多元时间序列预测中较先进模型提升20%-60%。
摘要：
多元时间序列（MTS）预测在天气、能源和金融等领域至关重要。然而，尽管深度学习取得了进展，传统的基于Transformer的模型往往通过单一令牌嵌入削弱了关键变量间关系的影响，并且难以有效捕捉变量之间的复杂依赖关系，尤其是在包含罕见或极端事件的数据集中。这些事件造成了显著的不平衡并导致高偏度，使准确预测工作复杂化。本研究引入了PFformer，一种基于无位置Transformer的模型，专为单目标MTS预测设计，特别针对以极端变异性为特征的挑战性数据集。PFformer整合了两种新颖的嵌入策略：增强型基于特征的嵌入（EFE）和基于自动编码器的嵌入（AEE）。EFE通过将相关序列子集映射到高维空间而无需位置约束，有效编码变量间依赖关系，增强了编码器的功能。PFformer在MTS建模中展现了优越的预测准确性，且没有传统位置编码的限制。我们在四个具有挑战性的数据集上评估了PFformer，重点关注两个关键预测场景：提前3天的长期序列预测和每四小时一次的滚动预测，以反映水资源管理中的实时决策过程。与最先进的模型相比，PFformer展现了从20%到60%的显著改进。
英文摘要：
Multivariate time series (MTS) forecasting is vital in fields like weather, energy, and finance. However, despite deep learning advancements, traditional Transformer-based models often diminish the effect of crucial inter-variable relationships by singular token embedding and struggle to effectively capture complex dependencies among variables, especially in datasets with rare or extreme events. These events create significant imbalances and lead to high skewness, complicating accurate prediction efforts. This study introduces PFformer, a position-free Transformer-based model designed for single-target MTS forecasting, specifically for challenging datasets characterized by extreme variability. PFformer integrates two novel embedding strategies: Enhanced Feature-based Embedding (EFE) and Auto-Encoder-based Embedding (AEE). EFE effectively encodes inter-variable dependencies by mapping related sequence subsets to high-dimensional spaces without positional constraints, enhancing the encoder's functionality. PFformer shows superior forecasting accuracy without the traditional limitations of positional encoding in MTS modeling. We evaluated PFformer across four challenging datasets, focusing on two key forecasting scenarios: long sequence prediction for 3 days ahead and rolling predictions every four hours to reflect real-time decision-making processes in water management. PFformer demonstrated remarkable improvements, from 20% to 60%, compared with state-of-the-art models.
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