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Life sciences · Preprint
arXiv · August 17, 2026
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AsyTO is a parameter-efficient deep learning operator for multivariate time series forecasting that factorizes per-variable predictors into shared temporal modes. The method achieves superior accuracy-to-compute trade-offs across 30 of 44 benchmark settings tested, but has not been peer-reviewed and lacks evaluation on clinical, financial, or mission-critical real-world forecasting tasks.
Algorithmic benchmarking study. Multivariate time series datasets from standard benchmarking suites; no description of dataset domains, sizes, or characteristics provided.. Intervention: AsyTO: Asymmetric Temporal Operator with factorized per-variable operators, shared temporal modes, mode-wise gains, periodic prototype, and cycle-separable factorization. Compared with: Dense phase-blind reference model and lag-invariant alternatives.
Asymmetric history-to-future maps outperform lag-invariant alternatives across tested settings AsyTO achieves best lightweight error in 30 of 44 dataset-horizon settings Method reduces parameter growth to linear rather than product of variables, context length, and horizon
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A novel machine learning architecture for time series forecasting with computational efficiency gains, but lacking clinical validation, real-world deployment evidence, or peer review.
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Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through an identical history-to-future map, whereas learning an independent predictor per variable restores flexibility at a cost that grows with the product of variable count, context length, and horizon. We argue that this dilemma dissolves once the object being compressed is the forecasting operator rather than the observed series. Auditing per-variable linear history-to-future maps across standard benchmarks, we find that a phase-locked seasonal component paired with a compact residual operator outperforms a dense phase-blind reference in most audited settings. The residual transport is also directional: lag-invariant alternatives consistently underperform asymmetric history-to-future maps. Guided by this structure, we propose AsyTO, an Asymmetric Temporal Operator that factorizes the tensor of per-variable operators into shared but distinct history-reading and future-writing temporal modes with per-variable mode-wise gains, complemented by a low-rank periodic prototype and a cycle-separable factorization of the temporal modes. Each forecast reads only its own variable's history, so parameters and compute grow linearly in the number of variables. Across eleven benchmarks and multiple forecast horizons, AsyTO attains the best lightweight error in 30 of 44 dataset-horizon settings, locating at the accuracy-compute Pareto frontier.
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