MiNiformer: Enhance Vanilla Transformer with Mixer-Adapter for Long-term Traffic Forecasting

Recently, there's been a surge in scholarly interest in traffic forecasting. Most of the efforts have been concentrated on short-term forecasting, and have yielded promising results. Long-term forecasting, though more practical, presents two challenges. First, existing approaches primarily capture dependencies and correlations within short-term historical data. Their performance drops when handling long-term spatio-temporal forecasting, indicating limited scalability. Second, most approaches tend to emphasize temporal information, often at the expense of neglecting important spatial geographic information. In response to these two challenges, we propose our transformer-based traffic forecasting approach, Miniformer, featuring the Spatial Feature Extractor - Mixer Adapter as a crucial element. Miniformer excels in extracting and integrating spatial features, leading to impressive results. Experiments show that Miniformer, by leveraging spatial information and long-term dependencies, showcases robust long-term feature extraction capabilities and performs exceptionally well in both short-term and long-term scenarios.