TrackGPT -- A generative pre-trained transformer for cross-domain entity
  trajectory forecasting
Nicholas Stroh
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.