EVOTS: Evolutionary Transformer Search for Time Series Forecasting
Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored. This paper introduces EVOTS, an evolutionary search framework that uses a modular genome representation and repair mechanism to discover task-adaptive Transformer-like models, achieving competitive results on ETT benchmarks.
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[Submitted on 30 Jun 2026]
Title:EVOTS: Evolutionary Transformer Search for Time Series Forecasting
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Abstract:Evolutionary neural architecture design for multivariate time-series forecasting remains underexplored, with most approaches relying on fixed Transformer architectures despite substantial variation across tasks and forecasting settings. This paper introduces an evolutionary neural architecture search framework for discovering task-adaptive Transformer-like models for time-series forecasting (EVOTS). Architectures are encoded using a modular genome representation that enables flexible composition of attention, feed-forward, and projection components, while a repair mechanism enforces structural validity throughout the evolutionary process. This formulation allows effective exploration of a diverse architecture space without relying on hand-crafted design rules. The proposed approach is evaluated on four benchmark datasets from the ETT family (ETTh1, ETTh2, ETTm1, and ETTm2) under multiple forecasting settings, including univariate-to-univariate, multivariate-to-univariate, and multivariate-to-multivariate prediction, with horizons of 96, 192, 336, and 720. In the multivariate-to-multivariate setting, the evolved architectures achieve competitive and, in several cases, improved mean squared error relative to a strong Transformer-based baseline. Additional analyses examine performance differences across forecasting settings and report wall-clock training time to provide a coarse indication of computational cost. Overall, the results demonstrate that evolutionary search can effectively discover flexible and high-performing Transformer-like architectures for multivariate time-series forecasting within practical runtime constraints.
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2607.00154 [cs.LG]
(or arXiv:2607.00154v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.00154
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3795095.3805078
DOI(s) linking to related resources
Submission history
From: AbdElRahman ElSaid [view email] [v1] Tue, 30 Jun 2026 20:29:34 UTC (424 KB)
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