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 multi-scale transformers with
adaptive pathways (Pathformer). The proposed Transformer 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.

该论文介绍了一种名为 Pathformer 的多尺度 Transformer 模型，通过自适应路径进行多尺度建模，以提高预测准确性和泛化能力，实验证明其在真实数据集上表现出优越的性能和泛化能力。