What is hierarchical attention in advanced time series transformer models?

Hierarchical attention models time series at multiple temporal resolutions using layered attention mechanisms.

Hierarchical attention decomposes time series into multiple scales (short-term, mid-term, long-term) and applies attention at each level. This allows models to capture both local fluctuations and global trends efficiently. It is widely used in advanced transformer-based forecasting architectures to improve scalability and interpretability.