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

TL;DR

This paper introduces an auto-configuration framework for multi-scale, multi-output time-series forecasting that balances prediction error and model complexity using evolutionary algorithms.

Contribution

It proposes a novel hierarchical search space and a Multi-Scale Bi-Branch CNN architecture for efficient, deployable forecasting models.

Findings

Outperforms baselines on synthetic and real datasets within limited budgets.

Provides a Pareto set of models balancing error and complexity.

Demonstrates flexible deployment options.

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.