\section{Conclusion, Limitation and Future Work}
\paragraph{Conclusion} In this paper, we propose FlightPatchNet, a multi-scale patch network with differential coding for the short-term FTP task. The differential coding is leveraged to reduce the significant differences in the original data range and reflect the temporal variations in realistic flight trajectories. The multi-scale patch network is designed to explore global trends and local details based on divided patches of different sizes, and integrate scale-wise correlations and inter-variable relationships for complete temporal modeling. Extensive experiments on a real-world dataset demonstrate that FlightPatchNet achieves the most competitive performance and offers a significant reduction in computational complexity, presenting a promising solution for real-time air traffic control applications.
\paragraph{Limitation and Future Work} The original series of altitude contains many unexpected noises. Our primary focus on modeling temporal variations enables FlightPatchNet to concentrate more on these irregular change points during altitude prediction, leading to a large bias. In the future, we will further explore temporal modeling of altitude and investigate robust noise-handling techniques such as moving average to make the series smoother and less sensitive to noise. In addition, the data-missing problem occurs commonly in realistic flight trajectory series, making  downstream analysis difficult. Thus, we attempt to incorporate data imputation methods into our model to enhance the model applicability and provide a general framework for flight trajectory prediction.