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

TL;DR

TrackGPT is a novel GPT-based model that accurately forecasts entity trajectories across multiple domains using minimal data, demonstrating superior performance and domain-agnostic capabilities compared to existing methods.

Contribution

This paper introduces TrackGPT, the first GPT-based model for entity trajectory forecasting that is domain-agnostic, requires minimal features, and outperforms state-of-the-art techniques.

Findings

TrackGPT achieves high accuracy in maritime and air trajectory forecasting.

It outperforms existing deep learning models in accuracy and reliability.

The model requires only location and time data, simplifying deployment.

Abstract

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.