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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Satellite Orbit PredictionLarge ModelTime-seriesTensor 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}

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Decomposed Attention FredFormer (DAF), a Transformer-based model using Real Fast Fourier Transform in place of positional embeddings and Tensorized Multi-Head Attention via Tensor Train Decomposition for parameter-efficient satellite orbit prediction. It resides in the 'Frequency-Domain and Fourier-Based Transformers' leaf, which contains only three papers total. This is a relatively sparse research direction within the broader taxonomy of fifty papers, suggesting that frequency-domain Transformer approaches for orbit prediction remain an emerging area rather than a crowded subfield.

The taxonomy reveals that DAF's immediate neighbors include 'Spatial-Temporal and Probsparse Attention Transformers' and 'Lightweight and Segment-Based Attention Models', both exploring alternative attention mechanisms for orbit forecasting. The broader parent category 'Transformer and Attention-Based Architectures' contrasts with sibling branches like 'LSTM-Based Orbit Prediction' and 'Hybrid Physics-Informed Methods'. DAF diverges from physics-informed approaches by relying on end-to-end learning, and from standard LSTM methods by leveraging frequency-domain representations and attention. Its position suggests a methodological bridge between classical time-domain recurrent models and more recent Transformer architectures.

Among eleven candidates examined, the Tensorized Multi-Head Attention contribution shows three refutable candidates, indicating that tensor decomposition techniques for attention compression have prior work in related domains. The DAF model itself and the zero-shot generalization claim show zero refutable candidates among the limited search scope. The analysis explicitly notes this is based on top-K semantic search plus citation expansion, not an exhaustive literature review. The frequency-domain Transformer contribution appears more novel within the examined set, while the tensor decomposition aspect has more substantial overlap with existing compression methods.

Given the limited search scope of eleven candidates and the sparse taxonomy leaf containing only three papers, the work appears to occupy a relatively underexplored niche in satellite orbit prediction. However, the tensor decomposition component shows measurable prior work, and the analysis cannot rule out additional relevant studies outside the examined candidate set. The zero-shot generalization claim for satellite orbit prediction lacks refuting evidence in this search, though the scope does not cover all possible foundation model or transfer learning literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
11
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: Satellite orbit prediction using time-series forecasting. The field has evolved into several distinct branches that reflect different methodological emphases and application contexts. Deep Learning Architectures for Orbit Prediction encompasses a range of neural network designs, from recurrent models like LSTM LEO Prediction[1] and TLE LSTM Network[4] to more recent transformer and attention-based approaches such as FDLSTM Frequency Domain[16] and Hybrid Frequency Temporal[49]. Hybrid Physics-Informed and Data-Driven Methods blend classical orbital mechanics with machine learning to improve generalization and physical consistency, while Uncertainty Quantification and Probabilistic Forecasting addresses the need for reliable confidence estimates in safety-critical space operations. Specialized Orbit Prediction Applications targets domain-specific challenges such as maneuver detection, constellation management, and cislunar trajectories, whereas Space Weather and Environmental Drivers focuses on modeling atmospheric drag and solar activity effects. Finally, Geodetic and Multi-Domain Time-Series Analysis extends beyond satellites to broader geospatial prediction tasks, often leveraging similar temporal modeling techniques. Within the transformer and attention-based architectures, a particularly active line of work explores frequency-domain representations to capture periodic orbital dynamics more effectively. Decomposed Attention FredFormer[0] sits squarely in this cluster, emphasizing Fourier-based decomposition to handle multi-scale temporal patterns inherent in satellite motion. This approach contrasts with purely time-domain recurrent methods like VMD RC LSTM[5], which rely on variational mode decomposition and reservoir computing, and aligns closely with FDLSTM Frequency Domain[16] and Hybrid Frequency Temporal[49], both of which similarly exploit spectral features. The main trade-off in this branch revolves around computational efficiency versus the ability to model long-range dependencies and complex periodicities. By integrating frequency-domain attention mechanisms, Decomposed Attention FredFormer[0] aims to balance these concerns, offering a middle ground between the interpretability of physics-informed models and the flexibility of end-to-end deep learning.

Claimed Contributions

Decomposed Attention FredFormer (DAF) model for satellite orbit prediction

The authors introduce DAF, a novel large time-series prediction model designed for satellite orbit forecasting. It replaces standard FFT with RFFT/IRFFT, removes patching and layer normalization, and uses positional embeddings to capture orbital periodicity efficiently.

1 retrieved paper
Tensorized Multi-Head Attention based on Tensor Train Decomposition

The authors develop a parameter-efficient attention mechanism by applying Tensor Train Decomposition to the multi-head attention weight matrices. This compression technique reduces model parameters while maintaining or improving prediction accuracy.

10 retrieved papers
Can Refute
First large time-series model for satellite orbit prediction with zero-shot generalization

The authors claim to be the first to propose a large-scale time-series model specifically for satellite orbit prediction. The model is pre-trained on Starlink data and demonstrates zero-shot prediction capability across diverse satellite constellations and real-world datasets without requiring individual model training per satellite.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Decomposed Attention FredFormer (DAF) model for satellite orbit prediction

The authors introduce DAF, a novel large time-series prediction model designed for satellite orbit forecasting. It replaces standard FFT with RFFT/IRFFT, removes patching and layer normalization, and uses positional embeddings to capture orbital periodicity efficiently.

Contribution

Tensorized Multi-Head Attention based on Tensor Train Decomposition

The authors develop a parameter-efficient attention mechanism by applying Tensor Train Decomposition to the multi-head attention weight matrices. This compression technique reduces model parameters while maintaining or improving prediction accuracy.

Contribution

First large time-series model for satellite orbit prediction with zero-shot generalization

The authors claim to be the first to propose a large-scale time-series model specifically for satellite orbit prediction. The model is pre-trained on Starlink data and demonstrates zero-shot prediction capability across diverse satellite constellations and real-world datasets without requiring individual model training per satellite.

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