Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecasting

• URL: http://arxiv.org/abs/2604.07610v1
• Date: Wed, 08 Apr 2026 21:28:30 GMT
• Title: Auto-Configured Networks for Multi-Scale Multi-Output Time-Series Forecasting
• Abstract summary: Industrial forecasting often involves multi-source asynchronous signals and multi-output targets.<n>Current practices fix alignment strategies or network designs, making it difficult to systematically co-design preprocessing.<n>We propose an auto-configuration framework that outputs a deployable Pareto set of forecasting models balancing error and complexity.
• Score: 7.883762084227456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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