EVOTS: Evolutionary Transformer Search for Time Series Forecasting

Summary

arXiv:2607.00154v1 Announce Type: new 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

SpinGraph analysis pending — check back after processing.

• How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size
• Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
• Token Geometry
• Geometry-Aware R-Structured Kolmogorov-Arnold Networks
• On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain
• Conditional Inference Trees and Forests for Feature Selection