提出统一框架，高效融合时间序列约束条件。
Forecasting time series with constraints
- 基于线性代数求解约束优化问题，计算高效。
- 在电力需求与旅游预测任务中达顶尖性能。
- 适合需强约束的工业级时序预测场景。
时间序列预测面临独特挑战，限制了传统机器学习算法的效果。为克服这些局限，已有方法将线性约束融入学习算法，如广义加性模型和层次化预测。本文提出一个统一框架，用于集成和组合时间序列预测中的线性约束。在该框架下，我们证明受约束的经验风险最小值可通过线性代数高效求解。该方法支持高度可扩展的GPU优化实现。通过在真实世界任务（包括电力需求预测和旅游预测）上的广泛基准测试验证了所提方法的有效性，达到当前最优性能。
原文摘要 · Abstract (English)
Time series forecasting presents unique challenges that limit the effectiveness of traditional machine learning algorithms. To address these limitations, various approaches have incorporated linear constraints into learning algorithms, such as generalized additive models and hierarchical forecasting. In this paper, we propose a unified framework for integrating and combining linear constraints in time series forecasting. Within this framework, we show that the exact minimizer of the constrained empirical risk can be computed efficiently using linear algebra alone. This approach allows for highly scalable implementations optimized for GPUs. We validate the proposed methodology through extensive benchmarking on real-world tasks, including electricity demand forecasting and tourism forecasting, achieving state-of-the-art performance.
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