Recently, Transformer-based models have shown remarkable performance in long-term time series forecasting (LTSF) tasks due to their ability to model long-term dependencies. However, the validity of Transformers for LTSF tasks remains debatable, particularly since recent work has shown that simple linear models can outperform numerous Transformer-based approaches. This suggests that there are limitations to the application of Transformer in LTSF. Therefore, this paper investigates three key issues when applying Transformer to LTSF: temporal continuity, information density, and multi-channel relationships. Accordingly, we propose three innovative solutions, including Placeholder Enhancement Technique (PET), Long Sub-sequence Division (LSD), and Multi-channel Separation and Interaction (MSI), which together form a novel model called PETformer. These three key designs introduce prior biases suitable for LTSF tasks. Extensive experiments have demonstrated that PETformer achieves state-of-the-art (SOTA) performance on eight commonly used public datasets for LTSF, outperforming all other models currently available. This demonstrates that Transformer still possesses powerful capabilities in LTSF.

该论文研究了在应用Transformer进行长期时间序列预测（LTSF）任务时的三个关键问题：时间连续性、信息密度和多通道关系，并提出了三种创新解决方案，构建了一种名为PETformer的新模型，通过广泛的实验证明PETformer在LTSF任务中取得了当前公共数据集上的最先进性能，优于其他所有现有模型，显示了Transformer在LTSF领域仍具有强大的能力。

PETformer: 基于占位符增强的长期时间序列预测变压器