Session
Session IV: Advanced Technology 2-Enterprise
Location
Salt Palace Convention Center, Salt Lake City, UT
Abstract
This paper introduces the notion of using a transformer-inspired time series forecasting model, more specifically IBM’s Tiny Time Mixer (TTM), to predict the covariance of Two-Line Elements (TLEs). The pre-trained compact univariate model is finetuned on a dataset of historical TLEs. Using the well established technique of pairwise differencing, a fixed-interval time series of TLE covariances is generated. This series is merged with exogenous variables extracted from the TLEs themselves, such as the classical orbital elements. The multi-level modeling capabilities of the TTM allow it to capture inter-channel correlations, crucial to capture the complex interactions inherent in the prediction of TLE covariances.
The TTM model is set up with generalizability in mind, training it on a dataset of satellites, completely distinct from the test set. This approach ensures that the model can generalize well to unseen satellites, making it applicable to a wide range of objects in Earth orbit. The performance of the model is demonstrated by assessing its ability to predict the covariance of TLEs for a distinct set of satellites. Across the different orbital regimes, the model demonstrates variable precision across the covariance components, with the tangential positional component remaining the most difficult to predict.
Document Type
Event
Predicting TLE Covariance Using Transformer-Based Time Series Forecasting
Salt Palace Convention Center, Salt Lake City, UT
This paper introduces the notion of using a transformer-inspired time series forecasting model, more specifically IBM’s Tiny Time Mixer (TTM), to predict the covariance of Two-Line Elements (TLEs). The pre-trained compact univariate model is finetuned on a dataset of historical TLEs. Using the well established technique of pairwise differencing, a fixed-interval time series of TLE covariances is generated. This series is merged with exogenous variables extracted from the TLEs themselves, such as the classical orbital elements. The multi-level modeling capabilities of the TTM allow it to capture inter-channel correlations, crucial to capture the complex interactions inherent in the prediction of TLE covariances.
The TTM model is set up with generalizability in mind, training it on a dataset of satellites, completely distinct from the test set. This approach ensures that the model can generalize well to unseen satellites, making it applicable to a wide range of objects in Earth orbit. The performance of the model is demonstrated by assessing its ability to predict the covariance of TLEs for a distinct set of satellites. Across the different orbital regimes, the model demonstrates variable precision across the covariance components, with the tangential positional component remaining the most difficult to predict.
