## 1 Introduction
Teams like An's and scholars like [PERSON] compared three methods, direct prediction, recursive prediction, and fusion prediction, and found that fusion prediction is always superior to or at least comparable with the best strategy among recursive and direct strategies. It avoids the difficult task of choosing between recursive and direct strategies, making it more competitive than these two strategies [9; 10]. However, the essence of these three strategies lies in the inability of traditional machine learning algorithms to handle multiple output problems normally. Still, deep learning can easily break through such limitations. Seq2 Seq is a deep learning framework that can be seen as a method for multiple output prediction. It was first proposed by Google Brain's [PERSON] and others to solve problems such as machine translation and a time series analysis [11]. As shown in Table 1 below, after comparing the above five models, the Seq2 Seq structure has obvious advantages over the first four methods. It is particularly effective at handling long sequences and is well suited for integration with deep learning. The Seq2 Seq model does, however, require high-quality training data. In our research, high-quality data refer to datasets that are comprehensive, accurately labeled, and representative of the problem space we are addressing. It is crucial that the data are also authentic, reflecting real-world scenarios and conditions. Obtaining such data is often a significant challenge. This is not only due to the need for extensive preprocessing and data cleaning to ensure the data capture the necessary variability without introducing noise, but also because of the necessity to establish connections with organizations that manage aviation and maritime data. Accessing authentic, detailed datasets from these organizations often involves navigating stringent data access policies and procedures, which can further complicate the data acquisition process. It is important to note that while a model trained on high-quality data tends to generalize better, the quality of testing and validation data still plays a crucial role in the model's performance. High-quality training data can improve the robustness of the model, but it cannot entirely compensate for poor-quality testing data. The model's performance may still degrade if the testing data do not adequately represent the conditions under which the model will be applied. In the Seq2 Seq structure, components like Long Short-Term Memory networks (LSTMs), Convolutional Neural Networks (CNNs), or Transformers are used for both the encoder and decoder. Traditional neural network structures, such as Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs), are designed to process specific types of input data effectively. However, these networks have significant limitations when it comes to handling temporal information. For instance, CNNs excel at image recognition tasks, while DNNs are adept at capturing complex patterns in static data. Despite their strengths, traditional neural networks struggle with sequence prediction tasks because they cannot utilize preceding event information to classify subsequent events. This limitation is particularly problematic in applications like speech recognition, where understanding the temporal sequence is crucial. For example, as mentioned earlier, feedforward neural networks can only provide a static mapping between input and output. To simulate time prediction tasks, we need a dynamic classifier [12]. Recurrent neural networks (RNNs) improve upon traditional neural networks by maintaining temporal information [13]. However, they suffer from gradient problems: gradients can vanish or explode over long sequences, which hampers learning. Due to these effects, RNNs are sensitive to time steps, meaning that they do not have long-term memory and are influenced primarily by short-term memory [14]. To address this issue, Long Short-Term Memory networks (LSTMs) were proposed.