Decomposed Attention FredFormer: Large Time-series Prediction Model for Satellite Orbit Prediction

Kangjun Lee, SeungWon Jeong, JongU Park, Youjin Shin, Simon S. Woo

Keywords: Satellite Orbit Prediction, Large Model, Time-series, Tensor Decomposition

Abstract:

Accurate satellite orbit prediction is critical for collision avoidance and sustainable space operations. However, conventional methods are constrained by coarse update intervals, orbit discontinuities, and other factors. Additionally, building separate prediction models for each satellite is computationally expensive, making large-scale accurate forecasting increasingly impractical.
To address the aforementioned challenges, we propose Decomposed Attention FredFormer (DAF), a large time‐series prediction model that uses efficient Real Fast Fourier Transform (RFFT)/Inverse RFFT in favor of positional embeddings. Our DAF also integrates Tensorized Multi-Head Attention based on Tensor Train Decomposition for parameter-efficient compression and improved performance. We pre-trained on a large‐scale Starlink dataset and evaluated zero-shot performance on seven cross-domain satellite orbit datasets and three real-world datasets. DAF achieves up to 34.85\% reduction in mean squared error and 16.01\% reduction in mean absolute error over the second-best model, using only 0.05\% of its parameters and maintaining inference time as fast as the conventional neural network baselines. These results demonstrate that DAF enables zero-shot, high-precision orbit prediction not only for Starlink satellites, but also for other satellites. The code is available here: \url{https://anonymous.4open.science/r/DAF-0D75}

Primary Area: learning on time series and dynamical systems

Submission Number: 1973