In recent years, numerous Transformer-based models have been applied to long-term time-series forecasting (LTSF) tasks. However, recent studies with linear models have questioned their effectiveness, demonstrating that simple linear layers can outperform sophisticated Transformer-based models. In this work, we review and categorize existing Transformer-based models into two main types: (1) modifications to the model structure and (2) modifications to the input data. The former offers scalability but falls short in capturing inter-sequential information, while the latter preprocesses time-series data but is challenging to use as a scalable module. We propose $\textbf{sTransformer}$, which introduces the Sequence and Temporal Convolutional Network (STCN) to fully capture both sequential and temporal information. Additionally, we introduce a Sequence-guided Mask Attention mechanism to capture global feature information. Our approach ensures the capture of inter-sequential information while maintaining module scalability. We compare our model with linear models and existing forecasting models on long-term time-series forecasting, achieving new state-of-the-art results. We also conducted experiments on other time-series tasks, achieving strong performance. These demonstrate that Transformer-based structures remain effective and our model can serve as a viable baseline for time-series tasks.

本研究针对现有Transformer模型在长期时间序列预测中的不足，提出了sTransformer，结合序列和时间卷积网络（STCN）以及序列引导的掩码注意机制，从而有效捕捉序列间和时间信息，同时保持模块可扩展性。研究结果表明，该模型在长期时间序列预测任务上超过了现有先进模型，为时间序列任务提供了可靠的基线。

sTransformer：用于时间序列预测的模块化方法，提取序列间和时间信息