全局模型比局部模型更准且更快，适合多层级时间序列预测。
Local vs. Global Models for Hierarchical Forecasting
- 用全局模型融合跨序列信息，提升预测一致性。
- 新提出的轻量级光梯度模型在多个层级上误差更低。
- 适合需要高效准确预测的供应链、金融等场景。
层级时间序列预测在多个领域决策中至关重要，但因存在多层级聚合、约束及信息可用性问题而具挑战性。本研究探讨不同信息利用方式对层级预测准确性的影响，提出并评估了局部模型与多种全局预测模型（GFMs）。与独立预测每条序列的局部模型不同，GFMs通过挖掘跨序列和跨层级信息，提升了预测性能与计算效率。采用校正方法确保预测一致性，并使用平均绝对缩放误差（MASE）和多重比较最优法（MCB）检验统计显著性。结果表明，GFMs在层级预测中具有显著优势，能在各层级提供更高精度与更优计算效率。本文提出两种基于LightGBM的特定GFMs，其精度优于对应局部模型及传统方法如指数平滑（ES）和自回归积分滑动平均（ARIMA），同时模型复杂度更低。
原文摘要 · Abstract (English)
Hierarchical time series forecasting plays a crucial role in decision-making in various domains while presenting significant challenges for modelling as they involve multiple levels of aggregation, constraints, and availability of information. This study explores the influence of distinct information utilisation on the accuracy of hierarchical forecasts, proposing and evaluating locals and a range of Global Forecasting Models (GFMs). In contrast to local models, which forecast each series independently, we develop GFMs to exploit cross-series and cross-hierarchies information, improving both forecasting performance and computational efficiency. We employ reconciliation methods to ensure coherency in forecasts and use the Mean Absolute Scaled Error (MASE) and Multiple Comparisons with the Best (MCB) tests to assess statistical significance. The findings indicate that GFMs possess significant advantages for hierarchical forecasting, providing more accurate and computationally efficient solutions across different levels in a hierarchy. Two specific GFMs based on LightGBM are introduced, demonstrating superior accuracy and lower model complexity than their counterpart local models and conventional methods such as Exponential Smoothing (ES) and Autoregressive Integrated Moving Average (ARIMA).
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