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.

翻译：工业预测通常涉及多源异步信号和多输出目标，而部署时需要在预测误差与模型复杂度之间进行明确的权衡。当前的做法通常固定对齐策略或网络设计，使得在预算受限且基于训练评估的场景中难以系统性地协同设计预处理、架构和超参数。为解决此问题，我们提出一种自动配置框架，能够输出一组可部署的预测模型帕累托集，平衡误差与复杂度。在模型层面，开发了多尺度双分支卷积神经网络（MS--BCNN），其中短核与长核分支分别捕获局部波动和长期趋势，用于多输出回归。在搜索层面，我们将对齐算子、架构选择和训练超参数统一纳入层次化条件混合配置空间，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误差-复杂度帕累托前沿。在分层合成基准和实际烧结数据集上的实验表明，我们的框架在相同预算下优于竞争基线，并提供了灵活的部署选择。