提出多层堆叠框架，显著提升时间序列预测精度。
Multi-layer Stack Ensembles for Time Series Forecasting
- 设计多层堆叠结构，融合多种模型预测优势。
- 在50个真实数据集上验证，准确率普遍优于传统方法。
- 适合需要高精度预测的自动化建模场景。
集成学习是提升机器学习模型性能的强大技术，堆叠方法在表格数据任务中表现优异。但在时间序列预测领域，集成方法仍应用不足，简单线性组合仍为最先进方法。本文系统研究了时间序列预测中的集成策略，在50个真实世界数据集上评估了33种集成模型（含现有与新方法）。结果表明，堆叠能持续提升预测准确率，但无单一堆叠器在所有任务中表现最佳。为此，我们提出一种面向时间序列预测的多层堆叠框架，通过结合不同堆叠模型的优势，实现对多样化预测场景的稳定高性能。研究结果凸显了基于堆叠的方法在提升时间序列自动机器学习系统方面的潜力。
原文摘要 · Abstract (English)
Ensembling is a powerful technique for improving the accuracy of machine learning models, with methods like stacking achieving strong results in tabular tasks. In time series forecasting, however, ensemble methods remain underutilized, with simple linear combinations still considered state-of-the-art. In this paper, we systematically explore ensembling strategies for time series forecasting. We evaluate 33 ensemble models -- both existing and novel -- across 50 real-world datasets. Our results show that stacking consistently improves accuracy, though no single stacker performs best across all tasks. To address this, we propose a multi-layer stacking framework for time series forecasting, an approach that combines the strengths of different stacker models. We demonstrate that this method consistently provides superior accuracy across diverse forecasting scenarios. Our findings highlight the potential of stacking-based methods to improve AutoML systems for time series forecasting.
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