自动配置多尺度多输出时间序列预测模型，平衡精度与复杂度。
Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecasting
- 设计多尺度双分支网络，分别捕捉局部波动与长期趋势。
- 在有限算力下找到误差与复杂度的最优权衡解集。
- 适合工业场景中资源受限的多源异步信号预测任务。
工业预测常涉及多源异步信号和多输出目标，部署时需在预测误差与模型复杂度间做出明确权衡。当前方法通常固定对齐策略或网络结构，难以在预算有限的训练评估中系统协同优化预处理、架构与超参数。为此，我们提出一种自动配置框架，输出可部署的误差-复杂度帕累托前沿模型集合。模型层面，构建多尺度双分支卷积神经网络（MS-BCNN），短核分支捕获局部波动，长核分支捕捉长期趋势，实现多输出回归。搜索层面，将对齐算子、架构选择与训练超参统一为分层条件混合配置空间，并采用基于玩家的混合多目标进化算法（PHMOEA）在有限计算预算内逼近帕累托前沿。在层级合成基准与真实烧结数据集上的实验表明，本框架在相同预算下优于对比基线，并提供灵活部署选项。
原文摘要 · Abstract (English)
Industrial forecasting often involves multi-source asynchronous signals and multi-output targets, while deployment requires explicit trade-offs between prediction error and model complexity. Current practices typically fix alignment strategies or network designs, making it difficult to systematically co-design preprocessing, architecture, and hyperparameters in budget-limited training-based evaluations. To address this issue, we propose an auto-configuration framework that outputs a deployable Pareto set of forecasting models balancing error and complexity. At the model level, a Multi-Scale Bi-Branch Convolutional Neural Network (MS--BCNN) is developed, where short- and long-kernel branches capture local fluctuations and long-term trends, respectively, for multi-output regression. At the search level, we unify alignment operators, architectural choices, and training hyperparameters into a hierarchical-conditional mixed configuration space, and apply Player-based Hybrid Multi-Objective Evolutionary Algorithm (PHMOEA) to approximate the error--complexity Pareto frontier within a limited computational budget. Experiments on hierarchical synthetic benchmarks and a real-world sintering dataset demonstrate that our framework outperforms competitive baselines under the same budget and offers flexible deployment choices.
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