Transformer-based models have achieved some success in time series forecasting. Existing methods mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales. In this paper, we propose Pathformer, a multi-scale transformer with adaptive pathways. The proposed Pathformer integrates both temporal resolution and temporal distance for multi-scale modeling. Multi-scale division divides the time series into different temporal resolutions using patches of various sizes. Based on the division of each scale, dual attention is performed over these patches to capture global correlations and local details as temporal dependencies. We further enrich the multi-scale transformer with adaptive pathways, which adaptively adjust the multi-scale modeling process based on the varying temporal dynamics in the input time series, improving the prediction accuracy and generalization of Pathformer. Extensive experiments on eleven real-world datasets demonstrate that Pathformer not only achieves state-of-the-art performance by surpassing all current models but also exhibits stronger generalization abilities under various transfer scenarios.

翻译：基于Transformer的模型在时间序列预测中已取得一定成功。现有方法主要从有限或固定尺度对时间序列进行建模，难以捕捉跨越不同尺度的多样特征。本文提出Pathformer——一种带有自适应路径的多尺度Transformer。该模型整合了时间分辨率与时间距离以实现多尺度建模：多尺度划分通过不同大小的补丁将时间序列分割为不同时间分辨率；基于每个尺度的划分，对这些补丁执行双重注意力机制，以捕捉全局关联与局部细节作为时间依赖关系。我们进一步通过自适应路径增强多尺度Transformer，使其能根据输入时间序列中变化的时序动态自适应调整多尺度建模过程，从而提升Pathformer的预测精度与泛化能力。在十一个真实数据集上的大量实验表明，Pathformer不仅超越现有所有模型达到最优性能，还在多种迁移场景下展现出更强的泛化能力。