Despite the success of DPM in practice, the mechanism behind it remains to be explored.

The main reasons come from the lack of concern on the feature correlation during interaction, and the limited receptive field. To remedy these deficiencies, this paper presents a Dual-Stream Interactive Transformer (DSIT) design.

While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting.

We adopt a multi-armed bandit framework for this process, enhancing prompt selection based on the returns from online trajectories.