Multimodal Multiobjective Neural Architecture Search for Lightweight and Failure-Resilient Time Series Forecasting
提出M3NAS-TSF框架，通过模块化超网和多模态多目标搜索自动设计轻量且抗故障的时间序列预测模型，在40个案例中RMSE最大降低14.50%。
Time series forecasting (TSF) plays a pivotal role in decision-making and risk mitigation by predicting future values from historical observations. Despite significant improvements in forecasting accuracy, existing TSF models face two critical challenges: increasing computational complexity hinders deployment on resource-constrained platforms, and vulnerability to random failures compromises prediction stability. To address these issues, we propose M3NAS-TSF, a multimodal multiobjective neural architecture search framework that automates the design of lightweight and failure-resilient TSF models. Specifically, to address computational complexity, we introduce a modular time series super-net that defines a flexible and compact search space, enabling the discovery of architectures with reduced model size. To enhance failure resilience, we develop a multimodal multiobjective neural architecture search algorithm that promotes architectural diversity through an architecture-aware niching strategy and an architectural diversity distance metric, ensuring a broad set of robust candidate models. We also propose a node distribution heatmap and a structural entropy index to assess architectural diversity without ground-truth Pareto sets. Extensive experiments on 40 forecasting cases demonstrate that M3NAS-TSF outperforms six representative baselines, achieving superior forecasting accuracy (the maximum reduction in the RMSE reached 14.50%) with a smaller model size while maintaining greater architectural diversity for failure resilience.