用元学习融合多模型预测，提升复杂季节性数据的准确性。
Combining Forecasts using Meta-Learning: A Comparative Study for Complex Seasonality
- 用多种机器学习模型做元学习，智能组合不同预测结果。
- 在多个复杂季节性任务上，性能显著优于简单平均法。
- 适合需要高精度时间序列预测的研究与工业场景。
本文研究了使用元学习融合不同类型模型生成的预测结果。传统组合方法多采用简单平均，而机器学习技术通过元学习可实现更复杂的组合策略，从而提升预测精度。我们采用线性回归、k近邻、多层感知机、随机森林和长短期记忆网络作为元学习器，并针对具有复杂季节性的时间序列定义了全局与局部元学习变体。在多个预测任务上的对比实验表明，该方法优于简单平均，显著提升了预测表现。
原文摘要 · Abstract (English)
In this paper, we investigate meta-learning for combining forecasts generated by models of different types. While typical approaches for combining forecasts involve simple averaging, machine learning techniques enable more sophisticated methods of combining through meta-learning, leading to improved forecasting accuracy. We use linear regression, $k$-nearest neighbors, multilayer perceptron, random forest, and long short-term memory as meta-learners. We define global and local meta-learning variants for time series with complex seasonality and compare meta-learners on multiple forecasting problems, demonstrating their superior performance compared to simple averaging.
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