标题： TrackGPT -- 一种用于跨领域实体轨迹预测的生成式预训练变换器 显示英文标题

标题： TrackGPT -- A generative pre-trained transformer for cross-domain entity trajectory forecasting

摘要： 在未来时间点预测实体轨迹是商业和国防领域应用中的关键能力差距。 变压器，特别是生成式预训练变压器（GPT）网络最近彻底改变了人工智能的几个领域，最著名的是自然语言处理（NLP），随着大型语言模型（LLM）如OpenAI的ChatGPT的出现。 在本研究论文中，我们介绍了TrackGPT，这是一种基于GPT的实体轨迹预测模型，在海洋和空中领域都显示出实用性，并且我们预计在其他领域也会表现良好。 TrackGPT作为一款开创性的GPT模型，能够在各种实体时间序列数据集上生成准确的预测，展示了在生成长期预测并保持准确性以及短期预测并具有高精度方面的熟练能力。 我们与最先进的深度学习技术进行了基准测试，结果表明TrackGPT的预测能力在准确性、可靠性和模块化方面表现出色。 重要的是，与达到类似性能的模型相比，TrackGPT在保持领域无关性的同时只需要最少的数据特征（仅位置和时间）。 总之，我们的研究结果强调了将GPT架构应用于实体轨迹预测任务的巨大潜力，这由创新的TrackGPT模型所体现。 显示英文摘要

摘要： The forecasting of entity trajectories at future points in time is a critical capability gap in applications across both Commercial and Defense sectors. Transformers, and specifically Generative Pre-trained Transformer (GPT) networks have recently revolutionized several fields of Artificial Intelligence, most notably Natural Language Processing (NLP) with the advent of Large Language Models (LLM) like OpenAI's ChatGPT. In this research paper, we introduce TrackGPT, a GPT-based model for entity trajectory forecasting that has shown utility across both maritime and air domains, and we expect to perform well in others. TrackGPT stands as a pioneering GPT model capable of producing accurate predictions across diverse entity time series datasets, demonstrating proficiency in generating both long-term forecasts with sustained accuracy and short-term forecasts with high precision. We present benchmarks against state-of-the-art deep learning techniques, showing that TrackGPT's forecasting capability excels in terms of accuracy, reliability, and modularity. Importantly, TrackGPT achieves these results while remaining domain-agnostic and requiring minimal data features (only location and time) compared to models achieving similar performance. In conclusion, our findings underscore the immense potential of applying GPT architectures to the task of entity trajectory forecasting, exemplified by the innovative TrackGPT model.